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 "video jigsaw unsupervised learning of spatiotemporal context for video action recognition"):
  Abstract: We propose a self-supervised learning method to jointly reason about spatial and temporal context for video recognition. Recent self-supervised approaches have used spatial context [9, 34] as well as temporal coherency [32] but a combination of the two requires extensive preprocessing such as tracking objects through millions of video frames [59] or computing optical flow to determine frame regions with high motion [30]. We propose to combine spatial and temporal context in one self-supervised framework without any heavy preprocessing. We divide multiple video frames into grids of patches and train a network to solve jigsaw puzzles on these patches from multiple frames. So the network is trained to correctly identify the position of a patch within a video frame as well as the position of a patch over time. We also propose a novel permutation strategy that outperforms random permutations while significantly reducing computational and memory constraints. We use our trained network for transfer learning tasks such as video activity recognition and demonstrate the strength of our approach on two benchmark video action recognition datasets without using a single frame from these datasets for unsupervised pretraining of our proposed video jigsaw network.

Includes members:
  Member "transitive invariance for self supervised visual representation learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning video object segmentation from unlabeled videos")
  Member "what actions are needed for understanding human actions in videos":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "evolving losses for unsupervised video representation learning")
  Member "unsupervised video understanding by reconciliation of posture similarities":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "unsupervised learning of action classes with continuous temporal embedding")
  Member "multi task self supervised visual learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a large scale study of representation learning with the visual task adaptation benchmark")
  Member "self supervised learning of pose embeddings from spatiotemporal relations in videos":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multiview consistent semi supervised learning for 3d human pose estimation")
  Member "the kinetics human action video dataset":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "transformation based adversarial video prediction on large scale data")
  Member "unsupervised representation learning by sorting sequences":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning representations by predicting bags of visual words")
  Member "cross domain self supervised multi task feature learning using synthetic imagery":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "unsupervised domain adaptation for mobile semantic segmentation based on cycle consistency and feature alignment")

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)