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: "modeling human motion with quaternion based neural networks"
  Abstract: Previous work on predicting or generating 3D human pose sequences regresses either joint rotations or joint positions. The former strategy is prone to error accumulation along the kinematic chain, as well as discontinuities when using Euler angles or exponential maps as parameterizations. The latter requires re-projection onto skeleton constraints to avoid bone stretching and invalid configurations. This work addresses both limitations. QuaterNet represents rotations with quaternions and our loss function performs forward kinematics on a skeleton to penalize absolute position errors instead of angle errors. We investigate both recurrent and convolutional architectures and evaluate on short-term prediction and long-term generation. For the latter, our approach is qualitatively judged as realistic as recent neural strategies from the graphics literature. Our experiments compare quaternions to Euler angles as well as exponential maps and show that only a very short context is required to make reliable future predictions. Finally, we show that the standard evaluation protocol for Human3.6M produces high variance results and we propose a simple solution.

Connected through hyperedge HE-1 (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"
    - "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"
    - "3d human pose estimation in video with temporal convolutions and semi supervised training"

Connected through hyperedge HE-2 (induced by source paper "back to the future joint aware temporal deep learning 3d human pose estimation"):
  with members:
    - "self supervised learning of 3d human pose using multi view geometry"
    - "semantic graph convolutional networks for 3d human pose regression"
    - "deep high resolution representation learning for human pose estimation"
    - "weakly supervised discovery of geometry aware representation for 3d human pose estimation"
    - "ordinal depth supervision for 3d human pose estimation"
    - "3d human pose estimation in video with temporal convolutions and semi supervised training"
    - "learning 3d human dynamics from video"
    - "2d 3d pose estimation and action recognition using multitask deep learning"

Connected through hyperedge HE-3 (induced by source paper "sampling good latent variables via cpp vaes vaes with condition posterior as prior"):
  with members:
    - "a surprisingly effective fix for deep latent variable modeling of text"
    - "implicit deep latent variable models for text generation"
    - "learning variations in human motion via mix and match perturbation"
    - "mixture content selection for diverse sequence generation"
    - "diverse trajectory forecasting with determinantal point processes"
    - "learning trajectory dependencies for human motion prediction"
    - "mixture models for diverse machine translation tricks of the trade"
    - "diversity sensitive conditional generative adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "learning variations in human motion via mix and match perturbation"):
  with members:
    - "action anticipation with rbf kernelized feature mapping rnn"
    - "videoflow a flow based generative model for video"
    - "human motion modeling using dvgans"
    - "bihmp gan bidirectional 3d human motion prediction gan"
    - "quaternet a quaternion based recurrent model for human motion"
    - "mt vae learning motion transformations to generate multimodal human dynamics"
    - "viena2 a driving anticipation dataset"
    - "a variational u net for conditional appearance and shape generation"

Connected through hyperedge HE-5 (induced by source paper "structured prediction helps 3d human motion modelling"):
  with members:
    - "amass archive of motion capture as surface shapes"
    - "bio lstm a biomechanically inspired recurrent neural network for 3 d pedestrian pose and gait prediction"
    - "quaternet a quaternion based recurrent model for human motion"
    - "deep inertial poser learning to reconstruct human pose from sparse inertial measurements in real time"
    - "hand pose estimation via latent 2 5d heatmap regression"
    - "cross modal deep variational hand pose estimation"
    - "learning to estimate 3d hand pose from single rgb images"
    - "learning human motion models for long term predictions"

Connected through hyperedge HE-6 (induced by source paper "human motion anticipation with symbolic label"):
  with members:
    - "learning trajectory dependencies for human motion prediction"
    - "forecasting future action sequences with neural memory networks"
    - "ms tcn multi stage temporal convolutional network for action segmentation"
    - "a neural temporal model for human motion prediction"
    - "quaternet a quaternion based recurrent model for human motion"
    - "convolutional sequence to sequence model for human dynamics"
    - "when will you do what anticipating temporal occurrences of activities"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"

Connected through hyperedge HE-7 (induced by source paper "trajectorylet net a novel framework for pose prediction based on trajectorylet descriptors"):
  with members:
    - "vred a position velocity recurrent encoder decoder for human motion prediction"
    - "human motion prediction via learning local structure representations and temporal dependencies"
    - "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-8 (induced by source paper "unsupervised and generic short term anticipation of human body motions"):
  with members:
    - "learning trajectory dependencies for human motion prediction"
    - "a neural temporal model for human motion prediction"
    - "convolutional sequence to sequence model for human dynamics"
    - "agi safety literature review"
    - "trellis networks for sequence modeling"
    - "gated complex recurrent neural networks"
    - "when will you do what anticipating temporal occurrences of activities"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"

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)