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 lstm and cnn"):
  Abstract: Recent methods based on 3D skeleton data have achieved outstanding performance due to its conciseness, robustness, and view-independent representation. With the development of deep learning, Convolutional Neural Networks (CNN) and Long Short Term Memory (LSTM)-based learning methods have achieved promising performance for action recognition. However, for CNN-based methods, it is inevitable to loss temporal information when a sequence is encoded into images. In order to capture as much spatial-temporal information as possible, LSTM and CNN are adopted to conduct effective recognition with later score fusion. In addition, experimental results show that the score fusion between CNN and LSTM performs better than that between LSTM and LSTM for the same feature. Our method achieved state-of-the-art results on NTU RGB+D datasets for 3D human action analysis. The proposed method achieved 87.40% in terms of accuracy and ranked $1^{st}$ place in Large Scale 3D Human Activity Analysis Challenge in Depth Videos.

Includes members:
  Member "a new representation of skeleton sequences for 3d action recognition":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a survey on 3d skeleton based action recognition using learning method")
  Member "joint distance maps based action recognition with convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep independently recurrent neural network indrnn")
  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-3 (induced by source paper "correlation net spatiotemporal multimodal deep learning for action recognition")
  Member "ntu rgb d a large scale dataset for 3d human activity analysis":
    also connected through other hyperedges:
      - Hyperedge HE-4 (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-5 (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-6 (induced by source paper "predictively encoded graph convolutional network for noise robust skeleton based action recognition")
  Member "action recognition based on joint trajectory maps using convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a survey on 3d skeleton based action recognition using learning method")
  Member "mining mid level features for action recognition based on effective skeleton representation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a deep learning approach for real time 3d human action recognition from skeletal data")

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)