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 hand gesture recognition by learning spd matrices with neural networks"):
  Abstract: In this paper, we propose a new hand gesture recognition method based on skeletal data by learning SPD matrices with neural networks. We model the hand skeleton as a graph and introduce a neural network for SPD matrix learning, taking as input the 3D coordinates of hand joints. The proposed network is based on two newly designed layers that transform a set of SPD matrices into a SPD matrix. For gesture recognition, we train a linear SVM classifier using features extracted from our network. Experimental results on a challenging dataset (Dynamic Hand Gesture dataset from the SHREC 2017 3D Shape Retrieval Contest) show that the proposed method outperforms state-of-the-art methods.

Includes members:
  Member "spatial temporal graph convolutional networks for skeleton based action recognition":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "unbiased scene graph generation from biased training")
  Member "graph edge convolutional neural networks for skeleton based action recognition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "adversarial attack on skeleton based human action recognition")
  Member "spatio temporal graph convolution for skeleton based action recognition":
    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 "feastnet feature steered graph convolutions for 3d shape analysis":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "π nets deep polynomial neural networks")
  Member "deepkspd learning kernel matrix based spd representation for fine grained image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a neural network based on spd manifold learning for skeleton based hand gesture recognition")
  Member "is second order information helpful for large scale visual recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "high order paired aspp networks for semantic segmenation")
  Member "motion feature augmented recurrent neural network for skeleton based dynamic hand gesture recognition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "vision based body gesture meta features for affective computing")
  Member "ntu rgb d a large scale dataset for 3d human activity analysis":
    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)