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 "towards generative adversarial networks as a new paradigm for radiology education"):
  Abstract: Medical students and radiology trainees typically view thousands of images in order to "train their eye" to detect the subtle visual patterns necessary for diagnosis. Nevertheless, infrastructural and legal constraints often make it difficult to access and quickly query an abundance of images with a user-specified feature set. In this paper, we use a conditional generative adversarial network (GAN) to synthesize $1024\times1024$ pixel pelvic radiographs that can be queried with conditioning on fracture status. We demonstrate that the conditional GAN learns features that distinguish fractures from non-fractures by training a convolutional neural network exclusively on images sampled from the GAN and achieving an AUC of $>0.95$ on a held-out set of real images. We conduct additional analysis of the images sampled from the GAN and describe ongoing work to validate educational efficacy.

Includes members:
  Member "synthetic data augmentation using gan for improved liver lesion classification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "imperfect imaganation implications of gans exacerbating biases on facial data augmentation and snapchat selfie lenses")
  Member "high resolution medical image synthesis using progressively grown generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "red blood cell image generation for data augmentation using conditional generative adversarial networks")
  Member "image synthesis in multi contrast mri with conditional generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "medgan medical image translation using gans")
  Member "high resolution mammogram synthesis using progressive generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "perceptual image super resolution with progressive adversarial network")
  Member "freehand ultrasound image simulation with spatially conditioned generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "augmenting correlation structures in spatial data using deep generative models")
  Member "progressive growing of gans for improved quality stability and variation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "a classification based study of covariate shift in gan distributions":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "classification accuracy score for conditional generative models")
  Member "stackgan realistic image synthesis with stacked generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "opengan open set generative adversarial 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)