human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "red blood cell image generation for data augmentation using conditional generative adversarial networks"):
  with members:
    - "learning more with less conditional pggan based data augmentation for brain metastases detection using highly rough annotation on mr images"
    - "generative adversarial network in medical imaging a review"
    - "gan augmentation augmenting training data using generative adversarial networks"
    - "medical image synthesis for data augmentation and anonymization using generative adversarial networks"
    - "deep adversarial context aware landmark detection for ultrasound imaging"
    - "synthetic data augmentation using gan for improved liver lesion classification"
    - "high resolution medical image synthesis using progressively grown generative adversarial networks"
    - "btrfly net vertebrae labelling with energy based adversarial learning of local spine prior"

Connected through hyperedge HE-2 (induced by source paper "unsupervised medical image translation using cycle medgan"):
  with members:
    - "cycle consistent adversarial denoising network for multiphase coronary ct angiography"
    - "adversarial inpainting of medical image modalities"
    - "generative adversarial network in medical imaging a review"
    - "retrospective correction of rigid and non rigid mr motion artifacts using gans"
    - "compressed sensing mri reconstruction using a generative adversarial network with a cyclic loss"
    - "stacked u nets a no frills approach to natural image segmentation"
    - "generative adversarial networks for image to image translation on multi contrast mr images a comparison of cyclegan and unit"
    - "the unreasonable effectiveness of deep features as a perceptual metric"

Connected through hyperedge HE-3 (induced by source paper "adversarial inpainting of medical image modalities"):
  with members:
    - "spg net segmentation prediction and guidance network for image inpainting"
    - "stacked u nets a no frills approach to natural image segmentation"
    - "perceptual adversarial networks for image to image transformation"
    - "generative image inpainting with contextual attention"
    - "generative face completion"
    - "perceptual losses for real time style transfer and super resolution"
    - "high resolution image inpainting using multi scale neural patch synthesis"
    - "image to image translation with conditional adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "an adversarial super resolution remedy for radar design trade offs"):
  with members:
    - "person identification and body mass index a deep learning based study on micro dopplers"
    - "adversarial inpainting of medical image modalities"
    - "retrospective correction of rigid and non rigid mr motion artifacts using gans"
    - "perceptual adversarial networks for image to image transformation"
    - "real time capable micro doppler signature decomposition of walking human limbs"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "image to image translation with conditional adversarial networks"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-5 (induced by source paper "retrospective correction of rigid and non rigid mr motion artifacts using gans"):
  with members:
    - "stacked u nets a no frills approach to natural image segmentation"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "brain tumor segmentation with deep neural networks"
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"
    - "image de raining using a conditional generative adversarial network"
    - "image to image translation with conditional adversarial networks"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-6 (induced by source paper "towards adversarial denoising of radar micro doppler signatures"):
  with members:
    - "adversarial inpainting of medical image modalities"
    - "cgans with projection discriminator"
    - "real time capable micro doppler signature decomposition of walking human limbs"
    - "perceptual losses for real time style transfer and super resolution"
    - "image to image translation with conditional adversarial networks"
    - "conditional generative adversarial nets"

Question: Please tell me which class the center node 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)