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 "skeleton based action recognition using spatio temporal lstm network with trust gates"):
  Abstract: Skeleton-based human action recognition has attracted a lot of research attention during the past few years. Recent works attempted to utilize recurrent neural networks to model the temporal dependencies between the 3D positional configurations of human body joints for better analysis of human activities in the skeletal data. The proposed work extends this idea to spatial domain as well as temporal domain to better analyze the hidden sources of action-related information within the human skeleton sequences in both of these domains simultaneously. Based on the pictorial structure of Kinect's skeletal data, an effective tree-structure based traversal framework is also proposed. In order to deal with the noise in the skeletal data, a new gating mechanism within LSTM module is introduced, with which the network can learn the reliability of the sequential data and accordingly adjust the effect of the input data on the updating procedure of the long-term context representation stored in the unit's memory cell. Moreover, we introduce a novel multi-modal feature fusion strategy within the LSTM unit in this paper. The comprehensive experimental results on seven challenging benchmark datasets for human action recognition demonstrate the effectiveness of the proposed method.

Includes members:
  Member "weakly supervised action learning with rnn based fine to coarse modeling":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "action graphs weakly supervised action localization with graph convolution networks")
  Member "learning and refining of privileged information based rnns for action recognition from depth sequences":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "centrality graph convolutional networks for skeleton based action recognition")
  Member "show and tell lessons learned from the 2015 mscoco image captioning challenge":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "aitpr attribute interaction tensor product representation for image caption")
  Member "rank pooling for action recognition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "short term temporal convolutional networks for dynamic hand gesture recognition")
  Member "unsupervised learning of long term motion dynamics for videos":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "disentangling physical dynamics from unknown factors for unsupervised video prediction")
  Member "cern confidence energy recurrent network for group activity recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "spatiotemporal relationship reasoning for pedestrian intent prediction")
  Member "ntu rgb d a large scale dataset for 3d human activity analysis":
    also connected through other hyperedges:
      - Hyperedge HE-7 (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-8 (induced by source paper "a survey on 3d skeleton based action recognition using learning method")

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)