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 "vred a position velocity recurrent encoder decoder for human motion prediction"):
  Abstract: Human motion prediction, which aims to predict future human poses given past poses, has recently seen increased interest. Many recent approaches are based on Recurrent Neural Networks (RNN) which model human poses with exponential maps. These approaches neglect the pose velocity as well as temporal relation of different poses, and tend to converge to the mean pose or fail to generate natural-looking poses. We therefore propose a novel Position-Velocity Recurrent Encoder-Decoder (PVRED) for human motion prediction, which makes full use of pose velocities and temporal positional information. A temporal position embedding method is presented and a Position-Velocity RNN (PVRNN) is proposed. We also emphasize the benefits of quaternion parameterization of poses and design a novel trainable Quaternion Transformation (QT) layer, which is combined with a robust loss function during training. Experiments on two human motion prediction benchmarks show that our approach considerably outperforms the state-of-the-art methods for both short-term prediction and long-term prediction. In particular, our proposed approach can predict future human-like and meaningful poses in 4000 milliseconds.

Includes members:
  Member "quaternet a quaternion based recurrent model for human motion":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "dlow diversifying latent flows for diverse human motion prediction")
  Member "convolutional sequence to sequence model for human dynamics":
    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 "action agnostic human pose forecasting":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "dlow diversifying latent flows for diverse human motion prediction")
  Member "neural kinematic networks for unsupervised motion retargetting":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "do as i do transferring human motion and appearance between monocular videos with spatial and temporal constraints")
  Member "long term human motion prediction by modeling motion context and enhancing motion dynamic":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "pisep 2 pseudo image sequence evolution based 3d pose prediction")
  Member "modeling temporal dynamics and spatial configurations of actions using two stream recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a survey on 3d skeleton based action recognition using learning method")
  Member "attention is all you need":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "learning human motion models for long term predictions":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "dlow diversifying latent flows for diverse human 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)