About sampling strategy and clustering setting

[RealNewNoob]

Congratulations on your publication! I am reading your code and paper, however, I have a question about the sampling policy.
In your paper, you mentioned M = 2, and N = 750, so two seeds, and their nearest 750 clusters are selected before CR, which makes a total of 1500.
However, in train_gcn.py line 146, the
for batch in range(cls_num): it seems all the clusters are looped, and for each of them, a total of 1300+200 = 1500 clusters are sampled before CR. In every training step, the features from these clusters are used to construct the affinity graph after SR.
Did I miss something?

[sstzal]

The "cls_num" in the code is the N in the paper.

You can refer this for more details. https://github.com/sstzal/STAR-FC/issues/3