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 "pisep 2 pseudo image sequence evolution based 3d pose prediction"):
  Abstract: Pose prediction is to predict future poses given a window of previous poses. In this paper, we propose a new problem that predicts poses using 3D joint coordinate sequences. Different from the traditional pose prediction based on Mocap frames, this problem is convenient to use in real applications due to its simple sensors to capture data. We also present a new framework, PISEP^2 (Pseudo Image Sequence Evolution based 3D Pose Prediction), to address this new problem. Specifically, a skeletal representation is proposed by transforming the joint coordinate sequence into an image sequence, which can model the different correlations of different joints. With this image based skeletal representation, we model the pose prediction as the evolution of image sequence. Moreover, a novel inference network is proposed to predict all future poses in one step by decoupling the decoders in a non-recursive manner. Compared with the recursive sequence to sequence model, we can improve the computational efficiency and avoid error accumulation significantly. Extensive experiments are carried out on two benchmark datasets (e.g. G3D and FNTU). The proposed method achieves the state-of-the-art performance on both datasets, which demonstrates the effectiveness of our proposed method.

Includes members:
  Member "frequency domain transformer networks for video prediction":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "utilizing temporal information in deep convolutional network for efficient soccer ball detection and tracking")
  Member "human action recognition and prediction a survey":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "action segmentation with joint self supervised temporal domain adaptation")
  Member "predrnn towards a resolution of the deep in time dilemma in spatiotemporal predictive learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "disentangling physical dynamics from unknown factors for unsupervised video prediction")
  Member "deep video generation prediction and completion of human action sequences":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "when relation networks meet gans relation gans with triplet loss")
  Member "stochastic adversarial video prediction":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "transformation based adversarial video prediction on large scale data")
  Member "co occurrence feature learning from skeleton data for action recognition and detection with hierarchical aggregation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a survey on 3d skeleton based action recognition using learning method")
  Member "timeception for complex action recognition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "ego topo environment affordances from egocentric video")
  Member "long term human motion prediction by modeling motion context and enhancing motion dynamic":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "spatiotemporal co attention recurrent neural networks for human skeleton motion prediction")

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)