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 visual representation for editing face images"
  Abstract: We propose a new approach for editing face images, which enables numerous exciting applications including face relighting, makeup transfer and face detail editing. Our face edits are based on a visual representation, which includes geometry, face segmentation, albedo, illumination and detail map. To recover our visual representation, we start by estimating geometry using a morphable face model, then decompose the face image to recover the albedo, and then shade the geometry with the albedo and illumination. The residual between our shaded geometry and the input image produces our detail map, which carries high frequency information that is either insufficiently or incorrectly captured by our shading process. By manipulating the detail map, we can edit face images with reality and identity preserved. Our representation allows various applications. First, it allows a user to directly manipulate various illumination. Second, it allows non-parametric makeup transfer with input face's distinctive identity features preserved. Third, it allows non-parametric modifications to the face appearance by transferring details. For face relighting and detail editing, we evaluate via a user study and our method outperforms other methods. For makeup transfer, we evaluate via an online attractiveness evaluation system, and can reliably make people look younger and more attractive. We also show extensive qualitative comparisons to existing methods, and have significant improvements over previous techniques.

Connected through hyperedge HE-1 (induced by source paper "ian combining generative adversarial networks for imaginative face generation"):
  with members:
    - "resembled generative adversarial networks two domains with similar attributes"
    - "multimodal unsupervised image to image translation"
    - "spectral normalization for generative adversarial networks"
    - "improving shape deformation in unsupervised image to image translation"
    - "crossing nets combining gans and vaes with a shared latent space for hand pose estimation"
    - "can creative adversarial networks generating art by learning about styles and deviating from style norms"
    - "began boundary equilibrium generative adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "inversefacenet deep single shot inverse face rendering from a single image"):
  with members:
    - "a comprehensive performance evaluation of deformable face tracking in the wild"
    - "3d face reconstruction with geometry details from a single image"
    - "large pose 3d face reconstruction from a single image via direct volumetric cnn regression"
    - "3d face morphable models in the wild"
    - "face alignment in the wild a survey"
    - "facial performance capture with deep neural networks"
    - "a recurrent encoder decoder network for sequential face alignment"
    - "failure detection for facial landmark detectors"

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)