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: "recurrent network models for human dynamics"
  Abstract: We propose the Encoder-Recurrent-Decoder (ERD) model for recognition and prediction of human body pose in videos and motion capture. The ERD model is a recurrent neural network that incorporates nonlinear encoder and decoder networks before and after recurrent layers. We test instantiations of ERD architectures in the tasks of motion capture (mocap) generation, body pose labeling and body pose forecasting in videos. Our model handles mocap training data across multiple subjects and activity domains, and synthesizes novel motions while avoid drifting for long periods of time. For human pose labeling, ERD outperforms a per frame body part detector by resolving left-right body part confusions. For video pose forecasting, ERD predicts body joint displacements across a temporal horizon of 400ms and outperforms a first order motion model based on optical flow. ERDs extend previous Long Short Term Memory (LSTM) models in the literature to jointly learn representations and their dynamics. Our experiments show such representation learning is crucial for both labeling and prediction in space-time. We find this is a distinguishing feature between the spatio-temporal visual domain in comparison to 1D text, speech or handwriting, where straightforward hard coded representations have shown excellent results when directly combined with recurrent units.

Connected through hyperedge HE-1 (induced by source paper "dlow diversifying latent flows for diverse human motion prediction"):
  with members:
    - "joint 3d tracking and forecasting with graph neural network and diversity sampling"
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "learning variations in human motion via mix and match perturbation"
    - "diverse trajectory forecasting with determinantal point processes"
    - "learning trajectory dependencies for human motion prediction"
    - "structured prediction helps 3d human motion modelling"
    - "imitation learning for human pose prediction"
    - "predicting 3d human dynamics from video"

Connected through hyperedge HE-2 (induced by source paper "movi a large multipurpose motion and video dataset"):
  with members:
    - "amass archive of motion capture as surface shapes"
    - "learning to reconstruct 3d human pose and shape via model fitting in the loop"
    - "expressive body capture 3d hands face and body from a single image"
    - "deep high resolution representation learning for human pose estimation"
    - "modeling human motion with quaternion based neural networks"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "quaternet a quaternion based recurrent model for human motion"
    - "learning to estimate 3d human pose and shape from a single color image"

Connected through hyperedge HE-3 (induced by source paper "dynamic multiscale graph neural networks for 3d skeleton based human motion prediction"):
  with members:
    - "3d point cloud processing and learning for autonomous driving"
    - "actional structural graph convolutional networks for skeleton based action recognition"
    - "learning trajectory dependencies for human motion prediction"
    - "symbiotic graph neural networks for 3d skeleton based human action recognition and motion prediction"
    - "imitation learning for human pose prediction"
    - "semantic graph convolutional networks for 3d human pose regression"
    - "human motion prediction via learning local structure representations and temporal dependencies"
    - "spatio temporal manifold learning for human motions via long horizon modeling"

Connected through hyperedge HE-4 (induced by source paper "machine learning approaches for motor learning a short review"):
  with members:
    - "human motion trajectory prediction a survey"
    - "vred a position velocity recurrent encoder decoder for human motion prediction"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "music2dance dancenet for music driven dance generation"):
  with members:
    - "dancing to music"
    - "learning individual styles of conversational gesture"
    - "a neural temporal model for human motion prediction"
    - "listen to dance music driven choreography generation using autoregressive encoder decoder network"
    - "convolutional sequence to sequence model for human dynamics"
    - "3d human pose estimation in video with temporal convolutions and semi supervised training"
    - "weakly supervised deep recurrent neural networks for basic dance step generation"
    - "onsets and frames dual objective piano transcription"

Connected through hyperedge HE-6 (induced by source paper "a deep learning framework for assessing physical rehabilitation exercises"):
  with members:
    - "a fine to coarse convolutional neural network for 3d human action recognition"
    - "co occurrence feature learning from skeleton data for action recognition and detection with hierarchical aggregation"
    - "dynamic temporal pyramid network a closer look at multi scale modeling for activity detection"
    - "temporal action detection with structured segment networks"
    - "deep representation learning for human motion prediction and classification"
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "temporal action localization in untrimmed videos via multi stage cnns"
    - "deepsense a unified deep learning framework for time series mobile sensing data processing"

Connected through hyperedge HE-7 (induced by source paper "music2dance music driven dance generation using wavenet"):
  with members:
    - "a neural temporal model for human motion prediction"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "listen to dance music driven choreography generation using autoregressive encoder decoder network"
    - "neural kinematic networks for unsupervised motion retargetting"
    - "weakly supervised deep recurrent neural networks for basic dance step generation"
    - "onsets and frames dual objective piano transcription"
    - "on human motion prediction using recurrent neural networks"
    - "auto conditioned recurrent networks for extended complex human motion synthesis"

Connected through hyperedge HE-8 (induced by source paper "clustering and recognition of spatiotemporal features through interpretable embedding of sequence to sequence recurrent neural networks"):
  with members:
    - "convolutional sequence to sequence model for human dynamics"
    - "on human motion prediction using recurrent neural networks"
    - "effective approaches to attention based neural machine translation"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "speech recognition with deep recurrent neural networks"

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)