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 "kg gan knowledge guided generative adversarial networks"):
  Abstract: Can generative adversarial networks (GANs) generate roses of various colors given only roses of red petals as input? The answer is negative, since GANs' discriminator would reject all roses of unseen petal colors. In this study, we propose knowledge-guided GAN (KG-GAN) to fuse domain knowledge with the GAN framework. KG-GAN trains two generators; one learns from data whereas the other learns from knowledge with a constraint function. Experimental results demonstrate the effectiveness of KG-GAN in generating unseen flower categories from seen categories given textual descriptions of the unseen ones.

Includes members:
  Member "mode seeking generative adversarial networks for diverse image synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "when relation networks meet gans relation gans with triplet loss")
  Member "f vaegan d2 a feature generating framework for any shot learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning to compare relation semantic alignment for few shot learning")
  Member "ian combining generative adversarial networks for imaginative face generation":
    also connected through other hyperedges:
      (none)
  Member "semantic image synthesis with spatially adaptive normalization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "gan compression efficient architectures for interactive conditional gans")
  Member "cgans with projection discriminator":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "object centric image generation from layouts")
  Member "multi modal cycle consistent generalized zero shot learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "latent embedding feedback and discriminative features for zero shot classification")
  Member "modular generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "towards unsupervised learning of generative models for 3d controllable image synthesis")
  Member "spectral normalization for generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (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)