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: "gazedirector fully articulated eye gaze redirection in video"
  Abstract: We present GazeDirector, a new approach for eye gaze redirection that uses model-fitting. Our method first tracks the eyes by fitting a multi-part eye region model to video frames using analysis-by-synthesis, thereby recovering eye region shape, texture, pose, and gaze simultaneously. It then redirects gaze by 1) warping the eyelids from the original image using a model-derived flow field, and 2) rendering and compositing synthesized 3D eyeballs onto the output image in a photorealistic manner. GazeDirector allows us to change where people are looking without person-specific training data, and with full articulation, i.e. we can precisely specify new gaze directions in 3D. Quantitatively, we evaluate both model-fitting and gaze synthesis, with experiments for gaze estimation and redirection on the Columbia gaze dataset. Qualitatively, we compare GazeDirector against recent work on gaze redirection, showing better results especially for large redirection angles. Finally, we demonstrate gaze redirection on YouTube videos by introducing new 3D gaze targets and by manipulating visual behavior.

Connected through hyperedge HE-1 (induced by source paper "photo realistic monocular gaze redirection using generative adversarial networks"):
  with members:
    - "generative adversarial networks for extreme learned image compression"
    - "multimodal unsupervised image to image translation"
    - "deep pictorial gaze estimation"
    - "reenactgan learning to reenact faces via boundary transfer"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "began boundary equilibrium generative adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-2 (induced by source paper "content consistent generation of realistic eyes with style"):
  with members:
    - "photo realistic monocular gaze redirection using generative adversarial networks"
    - "openeds open eye dataset"
    - "few shot adaptive gaze estimation"
    - "semantic image synthesis with spatially adaptive normalization"
    - "multimodal unsupervised image to image translation"
    - "a style based generator architecture for generative adversarial networks"
    - "learning to find eye region landmarks for remote gaze estimation in unconstrained settings"
    - "demystifying neural style transfer"

Connected through hyperedge HE-3 (induced by source paper "gazecorrection self guided eye manipulation in the wild using self supervised generative adversarial networks"):
  with members:
    - "multi channel attention selection gan with cascaded semantic guidance for cross view image translation"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "spectral normalization for generative adversarial networks"
    - "elegant exchanging latent encodings with gan for transferring multiple face attributes"
    - "ganimation anatomically aware facial animation from a single image"
    - "self supervised generative adversarial networks"
    - "everybody dance now"
    - "a fast rcnn hard positive generation via adversary for object detection"

Connected through hyperedge HE-4 (induced by source paper "improving few shot user specific gaze adaptation via gaze redirection synthesis"):
  with members:
    - "recurrent cnn for 3d gaze estimation using appearance and shape cues"
    - "deep pictorial gaze estimation"
    - "appearance based 3d gaze estimation with personal calibration"
    - "ganimation anatomically aware facial animation from a single image"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "mpiigaze real world dataset and deep appearance based gaze estimation"
    - "eye tracking for everyone"
    - "learning from simulated and unsupervised images through adversarial training"

Connected through hyperedge HE-5 (induced by source paper "eye contact correction using deep neural networks"):
  with members:
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "eye tracking for everyone"
    - "learning from simulated and unsupervised images through adversarial training"
    - "deepwarp photorealistic image resynthesis for gaze manipulation"
    - "it s written all over your face full face appearance based gaze estimation"
    - "u net convolutional networks for biomedical image segmentation"
    - "cyclical learning rates for training neural networks"
    - "unsupervised learning of visual structure using predictive generative networks"

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)