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: "capture learning and synthesis of 3d speaking styles"
  Abstract: Audio-driven 3D facial animation has been widely explored, but achieving realistic, human-like performance is still unsolved. This is due to the lack of available 3D datasets, models, and standard evaluation metrics. To address this, we introduce a unique 4D face dataset with about 29 minutes of 4D scans captured at 60 fps and synchronized audio from 12 speakers. We then train a neural network on our dataset that factors identity from facial motion. The learned model, VOCA (Voice Operated Character Animation) takes any speech signal as input - even speech in languages other than English - and realistically animates a wide range of adult faces. Conditioning on subject labels during training allows the model to learn a variety of realistic speaking styles. VOCA also provides animator controls to alter speaking style, identity-dependent facial shape, and pose (i.e. head, jaw, and eyeball rotations) during animation. To our knowledge, VOCA is the only realistic 3D facial animation model that is readily applicable to unseen subjects without retargeting. This makes VOCA suitable for tasks like in-game video, virtual reality avatars, or any scenario in which the speaker, speech, or language is not known in advance. We make the dataset and model available for research purposes at this http URL.

Connected through hyperedge HE-1 (induced by source paper "photorealistic lip sync with adversarial temporal convolutional networks"):
  with members:
    - "few shot adversarial learning of realistic neural talking head models"
    - "audio2face generating speech face animation from single audio with attention based bidirectional lstm networks"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "obamanet photo realistic lip sync from text"
    - "you said that"
    - "high resolution image synthesis and semantic manipulation with conditional gans"
    - "image to image translation with conditional adversarial networks"
    - "wavenet a generative model for raw audio"

Connected through hyperedge HE-2 (induced by source paper "animating face using disentangled audio representations"):
  with members:
    - "the conversation deep audio visual speech enhancement"
    - "lip movements generation at a glance"
    - "talking face generation by adversarially disentangled audio visual representation"
    - "lrs3 ted a large scale dataset for visual speech recognition"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "visemenet audio driven animator centric speech animation"
    - "speech driven facial reenactment using conditional generative adversarial networks"
    - "everybody dance now"

Connected through hyperedge HE-3 (induced by source paper "neural voice puppetry audio driven facial reenactment"):
  with members:
    - "deferred neural rendering image synthesis using neural textures"
    - "faceforensics learning to detect manipulated facial images"
    - "realistic speech driven facial animation with gans"
    - "text based editing of talking head video"
    - "neural style preserving visual dubbing"
    - "headon real time reenactment of human portrait videos"
    - "forensictransfer weakly supervised domain adaptation for forgery detection"
    - "deep appearance models for face rendering"

Connected through hyperedge HE-4 (induced by source paper "animation synthesis triggered by vocal mimics"):
  with members:
    - "realistic speech driven facial animation with gans"
    - "visemenet audio driven animator centric speech animation"

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)