Fix typo (#13098)

doc/source/memory-management.rst

@@ -18,7 +18,7 @@ Ray system memory: this is memory used internally by Ray
 
 Application memory: this is memory used by your application
   - **Worker heap**: memory used by your application (e.g., in Python code or TensorFlow), best measured as the *resident set size (RSS)* of your application minus its *shared memory usage (SHR)* in commands such as ``top``. The reason you need to subtract *SHR* is that object store shared memory is reported by the OS as shared with each worker. Not subtracting *SHR* will result in double counting memory usage.
-  - **Object store memory**: memory used when your application creates objects in the objects store via ``ray.put`` and when returning values from remote functions. Objects are reference counted and evicted when they fall out of scope. There is an object store server running on each node.
+  - **Object store memory**: memory used when your application creates objects in the object store via ``ray.put`` and when returning values from remote functions. Objects are reference counted and evicted when they fall out of scope. There is an object store server running on each node.
   - **Object store shared memory**: memory used when your application reads objects via ``ray.get``. Note that if an object is already present on the node, this does not cause additional allocations. This allows large objects to be efficiently shared among many actors and tasks.
 
 ObjectRef Reference Counting