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 "literature review of action recognition in the wild"):
  Abstract: The literature review presented below on Action Recognition in the wild is the in-depth study of Research Papers. Action Recognition problem in the untrimmed videos is a challenging task and most of the papers have tackled this problem using hand-crafted features with shallow learning techniques and sophisticated end-to-end deep learning techniques.

Includes members:
  Member "human action recognition and prediction a survey":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "action segmentation with joint self supervised temporal domain adaptation")
  Member "spatial temporal graph convolutional networks for skeleton based action recognition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "unbiased scene graph generation from biased training")
  Member "optimized skeleton based action recognition via sparsified graph regression":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "semantics guided neural networks for efficient skeleton based human action recognition")
  Member "temporal 3d convnets new architecture and transfer learning for video classification":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "view invariant deep architecture for human action recognition using two stream motion and shape temporal dynamics")
  Member "actionvlad learning spatio temporal aggregation for action classification":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "ego topo environment affordances from egocentric video")
  Member "co occurrence feature learning for skeleton based action recognition using regularized deep lstm networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "predictively encoded graph convolutional network for noise robust skeleton based action recognition")

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)