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: "speech driven facial reenactment using conditional generative adversarial networks"
  Abstract: We present a novel approach to generating photo-realistic images of a face with accurate lip sync, given an audio input. By using a recurrent neural network, we achieved mouth landmarks based on audio features. We exploited the power of conditional generative adversarial networks to produce highly-realistic face conditioned on a set of landmarks. These two networks together are capable of producing a sequence of natural faces in sync with an input audio track.

Connected through hyperedge HE-1 (induced by source paper "animating face using disentangled audio representations"):
  with members:
    - "capture learning and synthesis of 3d speaking styles"
    - "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"
    - "everybody dance now"

Connected through hyperedge HE-2 (induced by source paper "high resolution talking face generation via mutual information approximation"):
  with members:
    - "lip movements generation at a glance"
    - "talking face generation by adversarially disentangled audio visual representation"
    - "which training methods for gans do actually converge"
    - "obamanet photo realistic lip sync from text"
    - "ensemble estimation of mutual information"
    - "recurrent scale approximation for object detection in cnn"
    - "you said that"
    - "perceptual losses for real time style transfer and super resolution"

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)