human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "mw gan multi warping gan for caricature generation with multi style geometric exaggeration"):
  Abstract: Given an input face photo, the goal of caricature generation is to produce stylized, exaggerated caricatures that share the same identity as the photo. It requires simultaneous style transfer and shape exaggeration with rich diversity, and meanwhile preserving the identity of the input. To address this challenging problem, we propose a novel framework called Multi-Warping GAN (MW-GAN), including a style network and a geometric network that are designed to conduct style transfer and geometric exaggeration respectively. We bridge the gap between the style and landmarks of an image with corresponding latent code spaces by a dual way design, so as to generate caricatures with arbitrary styles and geometric exaggeration, which can be specified either through random sampling of latent code or from a given caricature sample. Besides, we apply identity preserving loss to both image space and landmark space, leading to a great improvement in quality of generated caricatures. Experiments show that caricatures generated by MW-GAN have better quality than existing methods.

Includes members:
  Member "carigans unpaired photo to caricature translation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "ganhopper multi hop gan for unsupervised image to image translation")
  Member "augmented cyclegan learning many to many mappings from unpaired data":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "augmented cyclic consistency regularization for unpaired image to image translation")
  Member "carigan caricature generation through weakly paired adversarial learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "fine grained image to image transformation towards visual recognition")
  Member "warpgan automatic caricature generation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "3d carigan an end to end solution to 3d caricature generation from face photos")
  Member "multimodal unsupervised image to image translation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "transformation based adversarial video prediction on large scale data")
  Member "gans trained by a two time scale update rule converge to a nash equilibrium":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "transformation based adversarial video prediction on large scale data")
  Member "triangle generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "one to one mapping for unpaired image to image translation")
  Member "arbitrary style transfer in real time with adaptive instance normalization":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "π nets deep polynomial neural networks")

Question: Please tell me which class the source paper 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)