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: "cyclegan face off"
  Abstract: Face-off is an interesting case of style transfer where the facial expressions and attributes of one person could be fully transformed to another face. We are interested in the unsupervised training process which only requires two sequences of unaligned video frames from each person and learns what shared attributes to extract automatically. In this project, we explored various improvements for adversarial training (i.e. CycleGAN[Zhu et al., 2017]) to capture details in facial expressions and head poses and thus generate transformation videos of higher consistency and stability.

Connected through hyperedge HE-1 (induced by source paper "a neural virtual anchor synthesizer based on seq2seq and gan models"):
  with members:
    - "video synthesis of human upper body with realistic face"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "one shot face reenactment"):
  with members:
    - "dense intrinsic appearance flow for human pose transfer"
    - "semantic image synthesis with spatially adaptive normalization"
    - "recovering realistic texture in image super resolution by deep spatial feature transform"
    - "facelet bank for fast portrait manipulation"
    - "synthesizing images of humans in unseen poses"
    - "talking face generation by adversarially disentangled audio visual representation"
    - "a variational u net for conditional appearance and shape generation"
    - "instance level facial attributes transfer with geometry aware flow"

Connected through hyperedge HE-3 (induced by source paper "faceswapnet landmark guided many to many face reenactment"):
  with members:
    - "pfld a practical facial landmark detector"
    - "look at boundary a boundary aware face alignment algorithm"
    - "soft gated warping gan for pose guided person image synthesis"
    - "reenactgan learning to reenact faces via boundary transfer"
    - "style aggregated network for facial landmark detection"
    - "a style based generator architecture for generative adversarial networks"
    - "a hybrid model for identity obfuscation by face replacement"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"

Connected through hyperedge HE-4 (induced by source paper "video synthesis of human upper body with realistic face"):
  with members:
    - "synthesizing images of humans in unseen poses"
    - "recycle gan unsupervised video retargeting"
    - "generating a fusion image one s identity and another s shape"
    - "reenactgan learning to reenact faces via boundary transfer"
    - "everybody dance now"
    - "learning to generate long term future via hierarchical prediction"
    - "pose guided person image generation"
    - "dynamics transfer gan generating video by transferring arbitrary temporal dynamics from a source video to a single target image"

Connected through hyperedge HE-5 (induced by source paper "reenactgan learning to reenact faces via boundary transfer"):
  with members:
    - "look at boundary a boundary aware face alignment algorithm"
    - "hallucinated iqa no reference image quality assessment via adversarial learning"
    - "disentangling 3d pose in a dendritic cnn for unconstrained 2d face alignment"
    - "densereg fully convolutional dense shape regression in the wild"
    - "style aggregated network for facial landmark detection"
    - "densepose dense human pose estimation in the wild"
    - "quantifying facial age by posterior of age comparisons"
    - "deep structured learning for facial action unit intensity estimation"

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)