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: "video synthesis of human upper body with realistic face"
  Abstract: This paper presents a generative adversarial learning-based human upper body video synthesis approach to generate an upper body video of target person that is consistent with the body motion, face expression, and pose of the person in source video. We use upper body keypoints, facial action units and poses as intermediate representations between source video and target video. Instead of directly transferring the source video to the target video, we firstly map the source person's facial action units and poses into the target person's facial landmarks, then combine the normalized upper body keypoints and generated facial landmarks with spatio-temporal smoothing to generate the corresponding target video's image. Experimental results demonstrated the effectiveness of our method.

Connected through hyperedge HE-1 (induced by source paper "a neural virtual anchor synthesizer based on seq2seq and gan models"):
  with members:
    - "recycle gan unsupervised video retargeting"
    - "deep contextualized word representations"
    - "reenactgan learning to reenact faces via boundary transfer"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a style based generator architecture for generative adversarial networks"
    - "everybody dance now"
    - "attention is all you need"
    - "cyclegan face off"

Connected through hyperedge HE-2 (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"
    - "a neural virtual anchor synthesizer based on seq2seq and gan models"
    - "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-3 (induced by source paper "a realistic face to face conversation system based on deep neural networks"):
  with members:
    - "a neural virtual anchor synthesizer based on seq2seq and gan models"
    - "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-4 (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"
    - "a neural virtual anchor synthesizer based on seq2seq and gan models"
    - "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)