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 "spatio temporal lstm with trust gates for 3d human action recognition"):
  Abstract: 3D action recognition - analysis of human actions based on 3D skeleton data - becomes popular recently due to its succinctness, robustness, and view-invariant representation. Recent attempts on this problem suggested to develop RNN-based learning methods to model the contextual dependency in the temporal domain. In this paper, we extend this idea to spatio-temporal domains to analyze the hidden sources of action-related information within the input data over both domains concurrently. Inspired by the graphical structure of the human skeleton, we further propose a more powerful tree-structure based traversal method. To handle the noise and occlusion in 3D skeleton data, we introduce new gating mechanism within LSTM to learn the reliability of the sequential input data and accordingly adjust its effect on updating the long-term context information stored in the memory cell. Our method achieves state-of-the-art performance on 4 challenging benchmark datasets for 3D human action analysis.

Includes members:
  Member "ntu rgb d a large scale dataset for 3d human activity analysis":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a survey on 3d skeleton based action recognition using learning method")
  Member "gated siamese convolutional neural network architecture for human re identification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "constrained dominant sets and its applications in computer vision")
  Member "co occurrence feature learning for skeleton based action recognition using regularized deep lstm networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "predictively encoded graph convolutional network for noise robust skeleton based action recognition")
  Member "online human action detection using joint classification regression recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a novel online action detection framework from untrimmed video streams")
  Member "deep multimodal feature analysis for action recognition in rgb d videos":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "infrared and 3d skeleton feature fusion for rgb d action recognition")
  Member "a siamese long short term memory architecture for human re identification":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "person recognition in personal photo collections")
  Member "a deep structured model with radius margin bound for 3d human activity recognition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning deep representations for semantic image parsing a comprehensive overview")
  Member "space time representation of people based on 3d skeletal data a review":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a fine to coarse convolutional neural network for 3d human 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)