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 "glyphgan style consistent font generation based on generative adversarial networks"):
  Abstract: In this paper, we propose GlyphGAN: style-consistent font generation based on generative adversarial networks (GANs). GANs are a framework for learning a generative model using a system of two neural networks competing with each other. One network generates synthetic images from random input vectors, and the other discriminates between synthetic and real images. The motivation of this study is to create new fonts using the GAN framework while maintaining style consistency over all characters. In GlyphGAN, the input vector for the generator network consists of two vectors: character class vector and style vector. The former is a one-hot vector and is associated with the character class of each sample image during training. The latter is a uniform random vector without supervised information. In this way, GlyphGAN can generate an infinite variety of fonts with the character and style independently controlled. Experimental results showed that fonts generated by GlyphGAN have style consistency and diversity different from the training images without losing their legibility.

Includes members:
  Member "automatic generation of typographic font from small font subset":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "neural style difference transfer and its application to font generation")
  Member "sharpness aware low dose ct denoising using conditional generative adversarial network":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "automatic catheter detection in pediatric x ray images using a scale recurrent network and synthetic data")
  Member "semi supervised fusedgan for conditional image generation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "hierarchical modes exploring in generative adversarial networks")
  Member "word level font to font image translation using convolutional recurrent generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "selective style transfer for text")
  Member "chinese typography transfer":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "stefann scene text editor using font adaptive neural network")
  Member "auto encoder guided gan for chinese calligraphy synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "ganwriting content conditioned generation of styled handwritten word images")
  Member "stackgan realistic image synthesis with stacked generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "opengan open set generative adversarial networks")
  Member "improved training of wasserstein gans":
    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)