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: "high resolution medical image synthesis using progressively grown generative adversarial networks"
  Abstract: Generative adversarial networks (GANs) are a class of unsupervised machine learning algorithms that can produce realistic images from randomly-sampled vectors in a multi-dimensional space. Until recently, it was not possible to generate realistic high-resolution images using GANs, which has limited their applicability to medical images that contain biomarkers only detectable at native resolution. Progressive growing of GANs is an approach wherein an image generator is trained to initially synthesize low resolution synthetic images (8x8 pixels), which are then fed to a discriminator that distinguishes these synthetic images from real downsampled images. Additional convolutional layers are then iteratively introduced to produce images at twice the previous resolution until the desired resolution is reached. In this work, we demonstrate that this approach can produce realistic medical images in two different domains; fundus photographs exhibiting vascular pathology associated with retinopathy of prematurity (ROP), and multi-modal magnetic resonance images of glioma. We also show that fine-grained details associated with pathology, such as retinal vessels or tumor heterogeneity, can be preserved and enhanced by including segmentation maps as additional channels. We envisage several applications of the approach, including image augmentation and unsupervised classification of pathology.

Connected through hyperedge HE-1 (induced by source paper "red blood cell image generation for data augmentation using conditional generative adversarial networks"):
  with members:
    - "medgan medical image translation using gans"
    - "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"
    - "btrfly net vertebrae labelling with energy based adversarial learning of local spine prior"

Connected through hyperedge HE-2 (induced by source paper "semi supervised learning approach to generate neuroimaging modalities with adversarial training"):
  with members:
    - "synergistic image and feature adaptation towards cross modality domain adaptation for medical image segmentation"
    - "unsupervised identification of disease marker candidates in retinal oct imaging data"
    - "generative adversarial networks for image to image translation on multi contrast mr images a comparison of cyclegan and unit"
    - "unsupervised domain adaptation for medical imaging segmentation with self ensembling"
    - "brain tumor segmentation with deep neural networks"
    - "cycada cycle consistent adversarial domain adaptation"
    - "adversarial discriminative domain adaptation"
    - "unsupervised image to image translation networks"

Connected through hyperedge HE-3 (induced by source paper "synthesizing new retinal symptom images by multiple generative models"):
  with members:
    - "a closed form solution to photorealistic image stylization"
    - "a novel hybrid machine learning model for auto classification of retinal diseases"
    - "auto classification of retinal diseases in the limit of sparse data using a two streams machine learning model"
    - "ganimation anatomically aware facial animation from a single image"
    - "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases"
    - "deep photo style transfer"
    - "pose normalized image generation for person re identification"
    - "synthetic medical images from dual generative adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "generative adversarial networks and adversarial methods in biomedical image analysis"):
  with members:
    - "compressed sensing mri reconstruction using a generative adversarial network with a cyclic loss"
    - "semantic aware generative adversarial nets for unsupervised domain adaptation in chest x ray segmentation"
    - "anatomically constrained neural networks acnns application to cardiac image enhancement and segmentation"
    - "low dose ct image denoising using a generative adversarial network with wasserstein distance and perceptual loss"
    - "distribution matching losses can hallucinate features in medical image translation"
    - "domain and geometry agnostic cnns for left atrium segmentation in 3d ultrasound"
    - "blood vessel geometry synthesis using generative adversarial networks"
    - "unsupervised cross modality domain adaptation of convnets for biomedical image segmentations with adversarial loss"

Connected through hyperedge HE-5 (induced by source paper "towards generative adversarial networks as a new paradigm for radiology education"):
  with members:
    - "synthetic data augmentation using gan for improved liver lesion classification"
    - "image synthesis in multi contrast mri with conditional generative adversarial networks"
    - "high resolution mammogram synthesis using progressive generative adversarial networks"
    - "freehand ultrasound image simulation with spatially conditioned generative adversarial networks"
    - "progressive growing of gans for improved quality stability and variation"
    - "a classification based study of covariate shift in gan distributions"
    - "stackgan realistic image synthesis with stacked generative adversarial networks"
    - "synthetic medical images from dual generative adversarial networks"

Connected through hyperedge HE-6 (induced by source paper "prostategan mitigating data bias via prostate diffusion imaging synthesis with generative adversarial networks"):
  with members:
    - "medical image synthesis for data augmentation and anonymization using generative adversarial networks"
    - "gan based synthetic medical image augmentation for increased cnn performance in liver lesion classification"
    - "adversarial networks for the detection of aggressive prostate cancer"
    - "deep mr to ct synthesis using unpaired data"
    - "infogan interpretable representation learning by information maximizing generative adversarial nets"
    - "improving computer aided detection using convolutional neural networks and random view aggregation"
    - "unsupervised representation learning with deep convolutional generative 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)