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 "joint distance maps based action recognition with convolutional neural networks"):
  Abstract: Motivated by the promising performance achieved by deep learning, an effective yet simple method is proposed to encode the spatio-temporal information of skeleton sequences into color texture images, referred to as joint distance maps (JDMs), and convolutional neural networks are employed to exploit the discriminative features from the JDMs for human action and interaction recognition. The pair-wise distances between joints over a sequence of single or multiple person skeletons are encoded into color variations to capture temporal information. The efficacy of the proposed method has been verified by the state-of-the-art results on the large RGB+D Dataset and small UTD-MHAD Dataset in both single-view and cross-view settings.

Includes members:
  Member "r c3d region convolutional 3d network for temporal activity detection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "weakly supervised temporal action localization using deep metric learning")
  Member "a new representation of skeleton sequences for 3d action recognition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a survey on 3d skeleton based action recognition using learning method")
  Member "view adaptive recurrent neural networks for high performance human action recognition from skeleton data":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "centrality graph convolutional networks for skeleton based action recognition")
  Member "scene flow to action map a new representation for rgb d based action recognition with convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "correlation net spatiotemporal multimodal deep learning for action recognition")
  Member "pku mmd a large scale benchmark for continuous multi modal human action understanding":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "infrared and 3d skeleton feature fusion for rgb d action recognition")
  Member "ntu rgb d a large scale dataset for 3d human activity analysis":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a survey on 3d skeleton based action recognition using learning method")
  Member "spatio temporal lstm with trust gates for 3d human action recognition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a survey on 3d skeleton based action recognition using learning method")
  Member "co occurrence feature learning for skeleton based action recognition using regularized deep lstm networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (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)