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: "learning human motion models for long term predictions"
  Abstract: We propose a new architecture for the learning of predictive spatio-temporal motion models from data alone. Our approach, dubbed the Dropout Autoencoder LSTM, is capable of synthesizing natural looking motion sequences over long time horizons without catastrophic drift or motion degradation. The model consists of two components, a 3-layer recurrent neural network to model temporal aspects and a novel auto-encoder that is trained to implicitly recover the spatial structure of the human skeleton via randomly removing information about joints during training time. This Dropout Autoencoder (D-AE) is then used to filter each predicted pose of the LSTM, reducing accumulation of error and hence drift over time. Furthermore, we propose new evaluation protocols to assess the quality of synthetic motion sequences even for which no ground truth data exists. The proposed protocols can be used to assess generated sequences of arbitrary length. Finally, we evaluate our proposed method on two of the largest motion-capture datasets available to date and show that our model outperforms the state-of-the-art on a variety of actions, including cyclic and acyclic motion, and that it can produce natural looking sequences over longer time horizons than previous methods.

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 "experimental evaluation of human motion prediction toward safe and efficient human robot collaboration"):
  with members:
    - "human motion prediction using semi adaptable neural networks"
    - "serocs safe and efficient robot collaborative systems for next generation intelligent industrial co robots"
    - "feature representation in convolutional neural networks"

Connected through hyperedge HE-6 (induced by source paper "modeling human motion with quaternion based neural networks"):
  with members:
    - "quaternion convolutional neural networks"
    - "a neural temporal model for human motion prediction"
    - "human motion modeling using dvgans"
    - "predicting future instance segmentations by forecasting convolutional features"
    - "quaternet a quaternion based recurrent model for human motion"
    - "convolutional sequence to sequence model for human dynamics"
    - "quaternion convolutional neural networks for end to end automatic speech recognition"
    - "3d human pose estimation in video with temporal convolutions and semi supervised training"

Connected through hyperedge HE-7 (induced by source paper "imitation learning for human pose prediction"):
  with members:
    - "procedure planning in instructional videos"
    - "peeking into the future predicting future person activities and locations in videos"
    - "mt vae learning motion transformations to generate multimodal human dynamics"
    - "action agnostic human pose forecasting"
    - "deepmimic example guided deep reinforcement learning of physics based character skills"
    - "learning plannable representations with causal infogan"
    - "predicting deeper into the future of semantic segmentation"
    - "hp gan probabilistic 3d human motion prediction via gan"

Connected through hyperedge HE-8 (induced by source paper "disentangling human dynamics for pedestrian locomotion forecasting with noisy supervision"):
  with members:
    - "uncertainty aware driver trajectory prediction at urban intersections"
    - "what the constant velocity model can teach us about pedestrian motion prediction"
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "egocentric vision based future vehicle localization for intelligent driving assistance systems"
    - "convolutional sequence to sequence model for human dynamics"
    - "action agnostic human pose forecasting"
    - "bdd100k a diverse driving video database with scalable annotation tooling"

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)