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 "unsupervised creation of parameterized avatars"):
  Abstract: We study the problem of mapping an input image to a tied pair consisting of a vector of parameters and an image that is created using a graphical engine from the vector of parameters. The mapping's objective is to have the output image as similar as possible to the input image. During training, no supervision is given in the form of matching inputs and outputs. #R##N#This learning problem extends two literature problems: unsupervised domain adaptation and cross domain transfer. We define a generalization bound that is based on discrepancy, and employ a GAN to implement a network solution that corresponds to this bound. Experimentally, our method is shown to solve the problem of automatically creating avatars.

Includes members:
  Member "a theory of output side unsupervised domain adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "neural inverse knitting from images to manufacturing instructions")
  Member "perceptual losses for real time style transfer and super resolution":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "gan compression efficient architectures for interactive conditional gans")
  Member "unsupervised cross domain image generation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "gan compression efficient architectures for interactive conditional gans")
  Member "texture networks feed forward synthesis of textures and stylized images":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning to adapt class specific features across domains for semantic segmentation")
  Member "generating images with perceptual similarity metrics based on deep networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "object centric image generation from layouts")
  Member "unsupervised learning of 3d structure from images":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "implicit functions in feature space for 3d shape reconstruction and completion")
  Member "generative adversarial text to image synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "image to image translation with conditional adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")

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)