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: "warpgan automatic caricature generation"
  Abstract: We propose, WarpGAN, a fully automatic network that can generate caricatures given an input face photo. Besides transferring rich texture styles, WarpGAN learns to automatically predict a set of control points that can warp the photo into a caricature, while preserving identity. We introduce an identity-preserving adversarial loss that aids the discriminator to distinguish between different subjects. Moreover, WarpGAN allows customization of the generated caricatures by controlling the exaggeration extent and the visual styles. Experimental results on a public domain dataset, WebCaricature, show that WarpGAN is capable of generating a diverse set of caricatures while preserving the identities. Five caricature experts suggest that caricatures generated by WarpGAN are visually similar to hand-drawn ones and only prominent facial features are exaggerated.

Connected through hyperedge HE-1 (induced by source paper "mw gan multi warping gan for caricature generation with multi style geometric exaggeration"):
  with members:
    - "carigans unpaired photo to caricature translation"
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "carigan caricature generation through weakly paired adversarial learning"
    - "multimodal unsupervised image to image translation"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "triangle generative adversarial networks"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "3d carigan an end to end solution to 3d caricature generation from face photos"):
  with members:
    - "carigans unpaired photo to caricature translation"
    - "maskgan towards diverse and interactive facial image manipulation"
    - "carigan caricature generation through weakly paired adversarial learning"
    - "generating 3d faces using convolutional mesh autoencoders"
    - "alive caricature from 2d to 3d"
    - "3d face reconstruction with geometry details from a single image"
    - "large pose 3d face reconstruction from a single image via direct volumetric cnn regression"
    - "deepsketch2face a deep learning based sketching system for 3d face and caricature modeling"

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)