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: "realistic speech driven facial animation with gans"
  Abstract: Speech-driven facial animation is the process that automatically synthesizes talking characters based on speech signals. The majority of work in this domain creates a mapping from audio features to visual features. This approach often requires post-processing using computer graphics techniques to produce realistic albeit subject dependent results. We present an end-to-end system that generates videos of a talking head, using only a still image of a person and an audio clip containing speech, without relying on handcrafted intermediate features. Our method generates videos which have (a) lip movements that are in sync with the audio and (b) natural facial expressions such as blinks and eyebrow movements. Our temporal GAN uses 3 discriminators focused on achieving detailed frames, audio-visual synchronization, and realistic expressions. We quantify the contribution of each component in our model using an ablation study and we provide insights into the latent representation of the model. The generated videos are evaluated based on sharpness, reconstruction quality, lip-reading accuracy, synchronization as well as their ability to generate natural blinks.

Connected through hyperedge HE-1 (induced by source paper "audio driven talking face video generation with learning based personalized head pose"):
  with members:
    - "coloring with limited data few shot colorization via memory augmented networks"
    - "ganfit generative adversarial network fitting for high fidelity 3d face reconstruction"
    - "neural voice puppetry audio driven facial reenactment"
    - "one shot face reenactment"
    - "accurate 3d face reconstruction with weakly supervised learning from single image to image set"
    - "speech2face learning the face behind a voice"
    - "lip movements generation at a glance"
    - "abstractive summarization of reddit posts with multi level memory networks"

Connected through hyperedge HE-2 (induced by source paper "moglow probabilistic and controllable motion synthesis using normalising flows"):
  with members:
    - "analyzing input and output representations for speech driven gesture generation"
    - "generating diverse high fidelity images with vq vae 2"
    - "fixup initialization residual learning without normalization"
    - "videoflow a flow based generative model for video"
    - "waveglow a flow based generative network for speech synthesis"
    - "quaternet a quaternion based recurrent model for human motion"
    - "flowavenet a generative flow for raw audio"
    - "efficient neural audio synthesis"

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"
    - "capture learning and synthesis of 3d speaking styles"
    - "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 "speech driven facial animation using polynomial fusion of features"):
  with members:
    - "polygan high order polynomial generators"
    - "voxceleb a large scale speaker identification dataset"
    - "mocogan decomposing motion and content for video generation"
    - "multi view machines"
    - "tensor networks for dimensionality reduction and large scale optimization part 1 low rank tensor decompositions"
    - "generating videos with scene dynamics"
    - "u net convolutional networks for biomedical image segmentation"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "emotion recognition system from speech and visual information based on convolutional neural networks"):
  with members:
    - "the omg emotion behavior dataset"
    - "deep facial expression recognition a survey"
    - "end to end multimodal emotion recognition using deep neural networks"
    - "dexpression deep convolutional neural network for expression recognition"
    - "emonets multimodal deep learning approaches for emotion recognition in video"
    - "adam a method for stochastic optimization"
    - "going deeper with convolutions"

Connected through hyperedge HE-6 (induced by source paper "animation synthesis triggered by vocal mimics"):
  with members:
    - "capture learning and synthesis of 3d speaking styles"
    - "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)