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: "a neural temporal model for human motion prediction"
  Abstract: We propose novel neural temporal models for predicting and synthesizing human motion, achieving state-of-the-art in modeling long-term motion trajectories while being competitive with prior work in short-term prediction and requiring significantly less computation. Key aspects of our proposed system include: 1) a novel, two-level processing architecture that aids in generating planned trajectories, 2) a simple set of easily computable features that integrate derivative information, and 3) a novel multi-objective loss function that helps the model to slowly progress from simple next-step prediction to the harder task of multi-step, closed-loop prediction. Our results demonstrate that these innovations improve the modeling of long-term motion trajectories. Finally, we propose a novel metric, called Normalized Power Spectrum Similarity (NPSS), to evaluate the long-term predictive ability of motion synthesis models, complementing the popular mean-squared error (MSE) measure of Euler joint angles over time. We conduct a user study to determine if the proposed NPSS correlates with human evaluation of long-term motion more strongly than MSE and find that it indeed does. We release code and additional results (visualizations) for this paper at: this https URL

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 "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-3 (induced by source paper "music2dance dancenet for music driven dance generation"):
  with members:
    - "dancing to music"
    - "learning individual styles of conversational gesture"
    - "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"
    - "on human motion prediction using recurrent neural networks"

Connected through hyperedge HE-4 (induced by source paper "music2dance music driven dance generation using wavenet"):
  with members:
    - "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"
    - "madmom a new python audio and music signal processing library"

Connected through hyperedge HE-5 (induced by source paper "modeling human motion with quaternion based neural networks"):
  with members:
    - "quaternion convolutional neural networks"
    - "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"
    - "neural kinematic networks for unsupervised motion retargetting"

Connected through hyperedge HE-6 (induced by source paper "symbiotic graph neural networks for 3d skeleton based human action recognition and motion prediction"):
  with members:
    - "action anticipation with rbf kernelized feature mapping rnn"
    - "an attention enhanced graph convolutional lstm network for skeleton based action recognition"
    - "human motion prediction via learning local structure representations and temporal dependencies"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "two stream adaptive graph convolutional networks for skeleton based action recognition"
    - "convolutional sequence to sequence model for human dynamics"
    - "look into person joint body parsing pose estimation network and a new benchmark"
    - "co occurrence feature learning from skeleton data for action recognition and detection with hierarchical aggregation"

Connected through hyperedge HE-7 (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"
    - "modeling human motion with quaternion based neural networks"
    - "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-8 (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"
    - "modeling human motion with quaternion based neural networks"
    - "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"

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)