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 medical image translation using cycle medgan"):
  Abstract: Image-to-image translation is a new field in computer vision with multiple potential applications in the medical domain. However, for supervised image translation frameworks, co-registered datasets, paired in a pixel-wise sense, are required. This is often difficult to acquire in realistic medical scenarios. On the other hand, unsupervised translation frameworks often result in blurred translated images with unrealistic details. In this work, we propose a new unsupervised translation framework which is titled Cycle-MedGAN. The proposed framework utilizes new non-adversarial cycle losses which direct the framework to minimize the textural and perceptual discrepancies in the translated images. Qualitative and quantitative comparisons against other unsupervised translation approaches demonstrate the performance of the proposed framework for PET-CT translation and MR motion correction.

Includes members:
  Member "medgan medical image translation using gans":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "red blood cell image generation for data augmentation using conditional generative adversarial networks")
  Member "cycle consistent adversarial denoising network for multiphase coronary ct angiography":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "optimal transport cyclegan and penalized ls for unsupervised learning in inverse problems")
  Member "adversarial inpainting of medical image modalities":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "image inpainting a review")
  Member "generative adversarial network in medical imaging a review":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "medgan medical image translation using gans")
  Member "retrospective correction of rigid and non rigid mr motion artifacts using gans":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "an adversarial super resolution remedy for radar design trade offs")
  Member "compressed sensing mri reconstruction using a generative adversarial network with a cyclic loss":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "medgan medical image translation using gans")
  Member "stacked u nets a no frills approach to natural image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "medgan medical image translation using gans")
  Member "generative adversarial networks for image to image translation on multi contrast mr images a comparison of cyclegan and unit":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "semi supervised learning approach to generate neuroimaging modalities with adversarial training")

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)