human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "self supervised convolutional subspace clustering network"):
  Abstract: Subspace clustering methods based on data self-expression have become very popular for learning from data that lie in a union of low-dimensional linear subspaces. However, the applicability of subspace clustering has been limited because practical visual data in raw form do not necessarily lie in such linear subspaces. On the other hand, while Convolutional Neural Network (ConvNet) has been demonstrated to be a powerful tool for extracting discriminative features from visual data, training such a ConvNet usually requires a large amount of labeled data, which are unavailable in subspace clustering applications. To achieve simultaneous feature learning and subspace clustering, we propose an end-to-end trainable framework, called Self-Supervised Convolutional Subspace Clustering Network (S$^2$ConvSCN), that combines a ConvNet module (for feature learning), a self-expression module (for subspace clustering) and a spectral clustering module (for self-supervision) into a joint optimization framework. Particularly, we introduce a dual self-supervision that exploits the output of spectral clustering to supervise the training of the feature learning module (via a classification loss) and the self-expression module (via a spectral clustering loss). Our experiments on four benchmark datasets show the effectiveness of the dual self-supervision and demonstrate superior performance of our proposed approach.

Includes members:
  Member "constrained sparse subspace clustering with side information":
    also connected through other hyperedges:
      (none)
  Member "structured sparse subspace clustering a joint affinity learning and subspace clustering framework":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "self expressive subspace clustering to recognize motion dynamics of a multi joint coordination for chronic ankle instability")
  Member "deep sparse subspace clustering":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multi graph fusion for multi view spectral clustering")
  Member "deep subspace clustering networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "gatcluster self supervised gaussian attention network for image clustering")
  Member "non linear subspace clustering with learned low rank kernels":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "scalable deep k subspace clustering")
  Member "ol e orthogonal low rank embedding a plug and play geometric loss for deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "concept whitening for interpretable image recognition")
  Member "adaptive low rank kernel subspace clustering":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "neural collaborative subspace clustering")
  Member "constructing the l2 graph for robust subspace learning and subspace clustering":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "ell_0 motivated low rank sparse subspace clustering")

Question: Please tell me which class the source paper belongs to. The 40 classes are: cs.NA(Numerical Analysis), cs.MM(Multimedia), cs.LO(Logic in Computer Science), cs.CY(Computers and Society), cs.CR(Cryptography and Security), cs.DC(Distributed, Parallel, and Cluster Computing), cs.HC(Human-Computer Interaction), cs.CE(Computational Engineering, Finance, and Science), cs.NI(Networking and Internet Architecture), cs.CC(Computational Complexity), cs.AI(Artificial Intelligence), cs.MA(Multiagent Systems), cs.GL(General Literature), cs.NE(Neural and Evolutionary Computing), cs.SC(Symbolic Computation), cs.AR(Hardware Architecture), cs.CV(Computer Vision and Pattern Recognition), cs.GR(Graphics), cs.ET(Emerging Technologies), cs.SY(Systems and Control), cs.CG(Computational Geometry), cs.OH(Other Computer Science), cs.PL(Programming Languages), cs.SE(Software Engineering), cs.LG(Machine Learning), cs.SD(Sound), cs.SI(Social and Information Networks), cs.RO(Robotics), cs.IT(Information Theory), cs.PF(Performance), cs.CL(Computation and Language), cs.IR(Information Retrieval), cs.MS(Mathematical Software), cs.FL(Formal Languages and Automata Theory), cs.DS(Data Structures and Algorithms), cs.OS(Operating Systems), cs.GT(Computer Science and Game Theory), cs.DB(Databases), cs.DL(Digital Libraries), cs.DM(Discrete Mathematics). Directly output the class name.

gpt: cs.CV(Computer Vision and Pattern Recognition)