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 virtual anchor synthesizer based on seq2seq and gan models"
  Abstract: This paper presents a novel framework to generate realistic face video of an anchor, who is reading certain news. This task is also known as Virtual Anchor. Given some paragraphs of words, we first utilize a pretrained Word2Vec model to embed each word into a vector; then we utilize a Seq2Seq-based model to translate these word embeddings into action units and head poses of the target anchor; these action units and head poses will be concatenated with facial landmarks as well as the former $n$ synthesized frames, and the concatenation serves as input of a Pix2PixHD-based model to synthesize realistic facial images for the virtual anchor. The experimental results demonstrate our framework is feasible for the synthesis of virtual anchor.

Connected through hyperedge HE-1 (induced by source paper "how old are you face age translation with identity preservation using gans"):
  with members:
    - "a realistic face to face conversation system based on deep neural networks"
    - "video synthesis of human upper body with realistic face"
    - "cross view image synthesis using conditional gans"
    - "look across elapse disentangled representation learning and photorealistic cross age face synthesis for age invariant face recognition"
    - "arcface additive angular margin loss for deep face recognition"
    - "global and local consistent age generative adversarial networks"
    - "sketchygan towards diverse and realistic sketch to image synthesis"
    - "a style based generator architecture for generative adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "a realistic face to face conversation system based on deep neural networks"):
  with members:
    - "video synthesis of human upper body with realistic face"
    - "facial pose estimation by deep learning from label distributions"
    - "video to video synthesis"
    - "a face to face neural conversation model"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "everybody dance now"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "facial pose estimation by deep learning from label distributions"):
  with members:
    - "a realistic face to face conversation system based on deep neural networks"
    - "video synthesis of human upper body with realistic face"
    - "detecting gaze towards eyes in natural social interactions and its use in child assessment"
    - "pose robust face recognition via deep residual equivariant mapping"
    - "fine grained head pose estimation without keypoints"
    - "deep label distribution learning with label ambiguity"
    - "how far are we from solving the 2d 3d face alignment problem and a dataset of 230 000 3d facial landmarks"
    - "kepler keypoint and pose estimation of unconstrained faces by learning efficient h cnn regressors"

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)