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 "trajectorylet net a novel framework for pose prediction based on trajectorylet descriptors"):
  Abstract: Pose prediction is an increasingly interesting topic in computer vision and robotics. In this paper, we propose a new network, Trajectorylet-Net, to predict future poses. Compared with most existing methods, our model focuses on modeling the co-occurrence long-term information and spatiotemporal correlation. Specifically, a novel descriptor, trajectorylet, is introduced to characterize the static and dynamic information of the input pose sequence. Then, a coupled spatio-temporal learning schema is proposed to generate trajectorylet descriptors, which can simultaneously capture the local structure of the human body and the global co-occurrence temporal information of the input sequence. Finally, we propose to predict future poses by gathering trajectorylet descriptors gradually. Extensive experiments show that our method achieves state-of-the-art performance on two benchmarks (e.g. G3D and FNTU), which demonstrates the effectiveness of our proposed method.

Includes members:
  Member "vred a position velocity recurrent encoder decoder for human motion prediction":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "machine learning approaches for motor learning a short review")
  Member "human motion prediction via learning local structure representations and temporal dependencies":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "dynamic multiscale graph neural networks for 3d skeleton based human motion prediction")
  Member "modeling human motion with quaternion based neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "movi a large multipurpose motion and video dataset")
  Member "a neural temporal model for human motion prediction":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "dlow diversifying latent flows for diverse human motion prediction")
  Member "a temporal sequence learning for action recognition and prediction":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "self attention network for skeleton based human action recognition")
  Member "convolutional sequence to sequence model for human dynamics":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "dynamic multiscale graph neural networks for 3d skeleton based human motion prediction")
  Member "action agnostic human pose forecasting":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "dlow diversifying latent flows for diverse human motion prediction")
  Member "co occurrence feature learning from skeleton data for action recognition and detection with hierarchical aggregation":
    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)