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 "a multi view perspective of self supervised learning"):
  Abstract: As a newly emerging unsupervised learning paradigm, self-supervised learning (SSL) recently gained widespread attention, which usually introduces a pretext task without manual annotation of data. With its help, SSL effectively learns the feature representation beneficial for downstream tasks. Thus the pretext task plays a key role. However, the study of its design, especially its essence currently is still open. In this paper, we borrow a multi-view perspective to decouple a class of popular pretext tasks into a combination of view data augmentation (VDA) and view label classification (VLC), where we attempt to explore the essence of such pretext task while providing some insights into its design. Specifically, a simple multi-view learning framework is specially designed (SSL-MV), which assists the feature learning of downstream tasks (original view) through the same tasks on the augmented views. SSL-MV focuses on VDA while abandons VLC, empirically uncovering that it is VDA rather than generally considered VLC that dominates the performance of such SSL. Additionally, thanks to replacing VLC with VDA tasks, SSL-MV also enables an integrated inference combining the predictions from the augmented views, further improving the performance. Experiments on several benchmark datasets demonstrate its advantages.

Includes members:
  Member "rethinking data augmentation self supervision and self distillation":
    also connected through other hyperedges:
      (none)
  Member "using self supervised learning can improve model robustness and uncertainty":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning representations by predicting bags of visual words")
  Member "self supervised visual feature learning with deep neural networks a survey":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "noise estimation using density estimation for self supervised multimodal learning")
  Member "unsupervised domain adaptation through self supervision":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multi modal domain adaptation for fine grained action recognition")
  Member "revisiting self supervised visual representation learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning representations by predicting bags of visual words")
  Member "self supervised gans via auxiliary rotation loss":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a large scale study of representation learning with the visual task adaptation benchmark")
  Member "deep clustering for unsupervised learning of visual features":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning representations by predicting bags of visual words")
  Member "unsupervised representation learning by predicting image rotations":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning representations by predicting bags of visual words")

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)