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: "long term human motion prediction by modeling motion context and enhancing motion dynamic"
  Abstract: Human motion prediction aims at generating future frames of human motion based on an observed sequence of skeletons. Recent methods employ the latest hidden states of a recurrent neural network (RNN) to encode the historical skeletons, which can only address short-term prediction. In this work, we propose a motion context modeling by summarizing the historical human motion with respect to the current prediction. A modified highway unit (MHU) is proposed for efficiently eliminating motionless joints and estimating next pose given the motion context. Furthermore, we enhance the motion dynamic by minimizing the gram matrix loss for long-term motion prediction. Experimental results show that the proposed model can promisingly forecast the human future movements, which yields superior performances over related state-of-the-art approaches. Moreover, specifying the motion context with the activity labels enables our model to perform human motion transfer.

Connected through hyperedge HE-1 (induced by source paper "pisep 2 pseudo image sequence evolution based 3d pose prediction"):
  with members:
    - "frequency domain transformer networks for video prediction"
    - "human action recognition and prediction a survey"
    - "predrnn towards a resolution of the deep in time dilemma in spatiotemporal predictive learning"
    - "deep video generation prediction and completion of human action sequences"
    - "stochastic adversarial video prediction"
    - "co occurrence feature learning from skeleton data for action recognition and detection with hierarchical aggregation"
    - "timeception for complex action recognition"
    - "learning to decompose and disentangle representations for video prediction"

Connected through hyperedge HE-2 (induced by source paper "spatiotemporal co attention recurrent neural networks for human skeleton motion prediction"):
  with members:
    - "hierarchical long short term concurrent memory for human interaction recognition"
    - "learning and refining of privileged information based rnns for action recognition from depth sequences"
    - "modeling temporal dynamics and spatial configurations of actions using two stream recurrent neural networks"
    - "deep representation learning for human motion prediction and classification"
    - "skeleton based action recognition using spatio temporal lstm network with trust gates"
    - "on human motion prediction using recurrent neural networks"
    - "view adaptive recurrent neural networks for high performance human action recognition from skeleton data"
    - "spatio temporal lstm with trust gates for 3d human action recognition"

Connected through hyperedge HE-3 (induced by source paper "falls prediction based on body keypoints and seq2seq architecture"):
  with members:
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "human action recognition and prediction a survey"
    - "convolutional sequence to sequence model for human dynamics"
    - "simple online and realtime tracking with a deep association metric"
    - "on human motion prediction using recurrent neural networks"
    - "temporal segment networks towards good practices for deep action recognition"
    - "recurrent network models for human dynamics"
    - "sequence to sequence video to text"

Connected through hyperedge HE-4 (induced by source paper "language2pose natural language grounded pose forecasting"):
  with members:
    - "generating animations from screenplays"
    - "pose guided human video generation"
    - "human motion modeling using dvgans"
    - "quaternet a quaternion based recurrent model for human motion"
    - "action agnostic human pose forecasting"
    - "learning a bidirectional mapping between human whole body motion and natural language using deep recurrent neural networks"
    - "learning human motion models for long term predictions"
    - "text2action generative adversarial synthesis from language to action"

Connected through hyperedge HE-5 (induced by source paper "human motion prediction via pattern completion in latent representation space"):
  with members:
    - "a neural temporal model for human motion prediction"
    - "human motion modeling using dvgans"
    - "quaternet a quaternion based recurrent model for human motion"
    - "convolutional sequence to sequence model for human dynamics"
    - "2d 3d pose estimation and action recognition using multitask deep learning"
    - "deep representation learning for human motion prediction and classification"
    - "on human motion prediction using recurrent neural networks"
    - "a hierarchical neural autoencoder for paragraphs and documents"

Connected through hyperedge HE-6 (induced by source paper "vred a position velocity recurrent encoder decoder for human motion prediction"):
  with members:
    - "quaternet a quaternion based recurrent model for human motion"
    - "convolutional sequence to sequence model for human dynamics"
    - "action agnostic human pose forecasting"
    - "neural kinematic networks for unsupervised motion retargetting"
    - "modeling temporal dynamics and spatial configurations of actions using two stream recurrent neural networks"
    - "attention is all you need"
    - "learning human motion models for long term predictions"
    - "deep representation learning for human motion prediction and classification"

Connected through hyperedge HE-7 (induced by source paper "predicting long term skeletal motions by a spatio temporal hierarchical recurrent network"):
  with members:
    - "convolutional sequence to sequence model for human dynamics"
    - "skeleton based action recognition using spatio temporal lstm network with trust gates"
    - "on human motion prediction using recurrent neural networks"
    - "unsupervised learning for physical interaction through video prediction"
    - "deep learning on lie groups for skeleton based action recognition"
    - "visual dynamics probabilistic future frame synthesis via cross convolutional networks"
    - "deep predictive coding networks for video prediction and unsupervised learning"
    - "lie x depth image based articulated object pose estimation tracking and action recognition on lie groups"

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)