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 "medgan medical image translation using gans"):
  Abstract: Abstract   Image-to-image translation is considered a new frontier in the field of medical image analysis, with numerous potential applications. However, a large portion of recent approaches offers individualized solutions based on specialized task-specific architectures or require refinement through non-end-to-end training. In this paper, we propose a new framework, named MedGAN, for medical image-to-image translation which operates on the image level in an end-to-end manner. MedGAN builds upon recent advances in the field of generative adversarial networks (GANs) by merging the adversarial framework with a new combination of non-adversarial losses. We utilize a discriminator network as a trainable feature extractor which penalizes the discrepancy between the translated medical images and the desired modalities. Moreover, style-transfer losses are utilized to match the textures and fine-structures of the desired target images to the translated images. Additionally, we present a new generator architecture, titled CasNet, which enhances the sharpness of the translated medical outputs through progressive refinement via encoder-decoder pairs. Without any application-specific modifications, we apply MedGAN on three different tasks: PET-CT translation, correction of MR motion artefacts and PET image denoising. Perceptual analysis by radiologists and quantitative evaluations illustrate that the MedGAN outperforms other existing translation approaches.

Includes members:
  Member "generative adversarial network in medical imaging a review":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on leveraging pretrained gans for limited data generation")
  Member "towards a deeper understanding of adversarial losses":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a novel framework for selection of gans for an application")
  Member "improving surgical training phantoms by hyperrealism deep unpaired image to image translation from real surgeries":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "cadis cataract dataset for image segmentation")
  Member "compressed sensing mri reconstruction using a generative adversarial network with a cyclic loss":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "geometric approaches to increase the expressivity of deep neural networks for mr reconstruction")
  Member "adversarial sparse view cbct artifact reduction":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "face completion with semantic knowledge and collaborative adversarial learning")
  Member "voxelatlasgan 3d left ventricle segmentation on echocardiography with atlas guided generation and voxel to voxel discrimination":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "tan temporal affine network for real time left ventricle anatomical structure analysis based on 2d ultrasound videos")
  Member "cross modality synthesis from ct to pet using fcn and gan networks for improved automated lesion detection":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "ct gan malicious tampering of 3d medical imagery using deep learning")
  Member "pros and cons of gan evaluation measures":
    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)