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: "human motion modeling using dvgans"
  Abstract: We present a novel generative model for human motion modeling using Generative Adversarial Networks (GANs). We formulate the GAN discriminator using dense validation at each time-scale and perturb the discriminator input to make it translation invariant. Our model is capable of motion generation and completion. We show through our evaluations the resiliency to noise, generalization over actions, and generation of long diverse sequences. We evaluate our approach on Human 3.6M and CMU motion capture datasets using inception scores.

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 "modeling human motion with quaternion based neural networks"):
  with members:
    - "quaternion convolutional neural networks"
    - "a neural temporal model for human motion prediction"
    - "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-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"
    - "modeling human motion with quaternion based neural networks"
    - "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 "language2pose natural language grounded pose forecasting"):
  with members:
    - "generating animations from screenplays"
    - "pose guided human video generation"
    - "quaternet a quaternion based recurrent model for human motion"
    - "action agnostic human pose forecasting"
    - "long term human motion prediction by modeling motion context and enhancing motion dynamic"
    - "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-6 (induced by source paper "human motion prediction via pattern completion in latent representation space"):
  with members:
    - "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"
    - "2d 3d pose estimation and action recognition using multitask deep learning"
    - "long term human motion prediction by modeling motion context and enhancing motion dynamic"
    - "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-7 (induced by source paper "human motion prediction via learning local structure representations and temporal dependencies"):
  with members:
    - "convolutional sequence to sequence model for human dynamics"
    - "deep representation learning for human motion prediction and classification"
    - "stackgan realistic image synthesis with stacked generative adversarial networks"
    - "on human motion prediction using recurrent neural networks"
    - "spatio temporal lstm with trust gates for 3d human action recognition"
    - "stacked generative adversarial networks"
    - "deep residual learning for image recognition"
    - "recurrent network models for human dynamics"

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)