human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "machine learning approaches for motor learning a short review"):
  with members:
    - "human motion trajectory prediction a survey"
    - "motion prediction with recurrent neural network dynamical models and trajectory optimization"
    - "generative adversarial self imitation learning"
    - "adapted deep embeddings a synthesis of methods for k shot inductive transfer learning"
    - "convolutional sequence to sequence model for human dynamics"
    - "reinforcement and imitation learning for diverse visuomotor skills"
    - "deep online learning via meta learning continual adaptation for model based rl"
    - "one shot imitation from observing humans via domain adaptive meta learning"

Connected through hyperedge HE-2 (induced by source paper "trajectorylet net a novel framework for pose prediction based on trajectorylet descriptors"):
  with members:
    - "human motion prediction via learning local structure representations and temporal dependencies"
    - "modeling human motion with quaternion based neural networks"
    - "a neural temporal model for human motion prediction"
    - "a temporal sequence learning for action recognition and prediction"
    - "convolutional sequence to sequence model for human dynamics"
    - "action agnostic human pose forecasting"
    - "co occurrence feature learning from skeleton data for action recognition and detection with hierarchical aggregation"
    - "deep representation learning for human motion prediction and classification"

Connected through hyperedge HE-3 (induced by source paper "prediction of human full body movements with motion optimization and recurrent neural networks"):
  with members:
    - "modeling human motion with quaternion based neural networks"
    - "learning human motion models for long term predictions"
    - "on human motion prediction using recurrent neural networks"
    - "functional gradient motion planning in reproducing kernel hilbert spaces"
    - "structural rnn deep learning on spatio temporal graphs"
    - "recurrent network models for human dynamics"
    - "newton methods for k order markov constrained motion problems"

Question: Please tell me which class the center node 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)