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 "multi modal self supervision from generalized data transformations"):
  Abstract: Self-supervised learning has advanced rapidly, with several results beating supervised models for pre-training feature representations. While the focus of most of these works has been new loss functions or tasks, little attention has been given to the data transformations that build the foundation of learning representations with desirable invariances. In this work, we introduce a framework for multi-modal data transformations that preserve semantics and induce the learning of high-level representations across modalities. We do this by combining two steps: inter-modality slicing, and intra-modality augmentation. Using a contrastive loss as the training task, we show that choosing the right transformations is key and that our method yields state-of-the-art results on downstream video and audio classification tasks such as HMDB51, UCF101 and DCASE2014 with Kinetics-400 pretraining.

Includes members:
  Member "a simple framework for contrastive learning of visual representations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "convergence of end to end training in deep unsupervised contrasitive learning")
  Member "audiovisual slowfast networks for video recognition":
    also connected through other hyperedges:
      (none)
  Member "learning spatiotemporal features via video and text pair discrimination":
    also connected through other hyperedges:
      (none)
  Member "on mutual information maximization for representation learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a large scale study of representation learning with the visual task adaptation benchmark")
  Member "self supervised spatio temporal representation learning for videos by predicting motion and appearance statistics":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning spatiotemporal features via video and text pair discrimination")
  Member "end to end learning of visual representations from uncurated instructional videos":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "noise estimation using density estimation for self supervised multimodal learning")
  Member "learning video representations using contrastive bidirectional transformer":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "univilm a unified video and language pre training model for multimodal understanding and generation")
  Member "contrastive multiview coding":
    also connected through other hyperedges:
      - Hyperedge HE-6 (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)