[doc] rename titles (#6608)

tutorials/large/L0_neighbor_sampling_overview.py

 """
-Introduction of Neighbor Sampling for GNN Training
-==================================================
+Introduction of Neighbor Sampling
+=================================
 
 In :doc:`previous tutorials <../blitz/1_introduction>` you have learned how to
 train GNNs by computing the representations of all nodes on a graph.

tutorials/large/L1_large_node_classification.py

 """
-Training GNN with Neighbor Sampling for Node Classification
+Node Classification
 ===========================================================
 
 This tutorial shows how to train a multi-layer GraphSAGE for node
@@ -42,7 +42,7 @@ device = "cpu"  # change to 'cuda' for GPU
 # training-validation-test set from the tasks. Seed nodes and corresponding
 # labels are already stored in each training-validation-test set. Other
 # metadata such as number of classes are also stored in the tasks. In this
-# dataset, there is only one task: `node classification``.
+# dataset, there is only one task: `node classification`.
 #
 
 graph = dataset.graph

tutorials/large/L2_large_link_prediction.py

 """
-Stochastic Training of GNN for Link Prediction
+Link Prediction
 ==============================================
 
 This tutorial will show how to train a multi-layer GraphSAGE for link