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 "spatiotemporal co attention recurrent neural networks for human skeleton motion prediction"):
  Abstract: Human motion prediction aims to generate future motions based on the observed human motions. Witnessing the success of Recurrent Neural Networks (RNN) in modeling the sequential data, recent works utilize RNN to model human-skeleton motion on the observed motion sequence and predict future human motions. However, these methods did not consider the existence of the spatial coherence among joints and the temporal evolution among skeletons, which reflects the crucial characteristics of human motion in spatiotemporal space. To this end, we propose a novel Skeleton-joint Co-attention Recurrent Neural Networks (SC-RNN) to capture the spatial coherence among joints, and the temporal evolution among skeletons simultaneously on a skeleton-joint co-attention feature map in spatiotemporal space. First, a skeleton-joint feature map is constructed as the representation of the observed motion sequence. Second, we design a new Skeleton-joint Co-Attention (SCA) mechanism to dynamically learn a skeleton-joint co-attention feature map of this skeleton-joint feature map, which can refine the useful observed motion information to predict one future motion. Third, a variant of GRU embedded with SCA collaboratively models the human-skeleton motion and human-joint motion in spatiotemporal space by regarding the skeleton-joint co-attention feature map as the motion context. Experimental results on human motion prediction demonstrate the proposed method outperforms the related methods.

Includes members:
  Member "hierarchical long short term concurrent memory for human interaction recognition":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "interaction relational network for mutual action recognition")
  Member "long term human motion prediction by modeling motion context and enhancing motion dynamic":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "pisep 2 pseudo image sequence evolution based 3d pose prediction")
  Member "learning and refining of privileged information based rnns for action recognition from depth sequences":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "centrality graph convolutional networks for skeleton based action recognition")
  Member "modeling temporal dynamics and spatial configurations of actions using two stream recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a survey on 3d skeleton based action recognition using learning method")
  Member "deep representation learning for human motion prediction and classification":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "dlow diversifying latent flows for diverse human motion prediction")
  Member "skeleton based action recognition using spatio temporal lstm network with trust gates":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "feedback graph convolutional network for skeleton based action recognition")
  Member "on human motion prediction using recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "dlow diversifying latent flows for diverse human motion prediction")
  Member "view adaptive recurrent neural networks for high performance human action recognition from skeleton data":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "centrality graph convolutional networks for 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)