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: "gans for medical image analysis"
  Abstract: Generative Adversarial Networks (GANs) and their extensions have carved open many exciting ways to tackle well known and challenging medical image analysis problems such as medical image de-noising, reconstruction, segmentation, data simulation, detection or classification. Furthermore, their ability to synthesize images at unprecedented levels of realism also gives hope that the chronic scarcity of labeled data in the medical field can be resolved with the help of these generative models. In this review paper, a broad overview of recent literature on GANs for medical applications is given, the shortcomings and opportunities of the proposed methods are thoroughly discussed and potential future work is elaborated. We review the most relevant papers published until the submission date. For quick access, important details such as the underlying method, datasets and performance are tabulated. An interactive visualization which categorizes all papers to keep the review alive, is available at this http URL

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"
    - "high resolution medical image synthesis using progressively grown generative adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "style transfer based image synthesis as an efficient regularization technique in deep learning"):
  with members:
    - "regularization for deep learning a taxonomy"
    - "text to image synthesis using generative adversarial networks"
    - "staingan stain style transfer for digital histological images"
    - "random erasing data augmentation"
    - "data driven color augmentation techniques for deep skin image analysis"
    - "the effectiveness of data augmentation in image classification using deep learning"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "neural style transfer a review"

Connected through hyperedge HE-3 (induced by source paper "refacing reconstructing anonymized facial features using gans"):
  with members:
    - "generative adversarial network in medical imaging a review"
    - "generative adversarial networks for image to image translation on multi contrast mr images a comparison of cyclegan and unit"
    - "learning myelin content in multiple sclerosis from multimodal mri through adversarial training"
    - "image synthesis in multi contrast mri with conditional generative adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "a survey on deep learning in medical image analysis"
    - "deep mr to ct synthesis using unpaired data"
    - "medical image synthesis with context aware generative adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "cia net robust nuclei instance segmentation with contour aware information aggregation"):
  with members:
    - "robust learning at noisy labeled medical images applied to skin lesion classification"
    - "generative adversarial network in medical imaging a review"
    - "path aggregation network for instance segmentation"
    - "decoupled weight decay regularization"
    - "mentornet learning data driven curriculum for very deep neural networks on corrupted labels"
    - "feature pyramid networks for object detection"
    - "u net convolutional networks for biomedical image segmentation"
    - "training deep neural networks on noisy labels with bootstrapping"

Connected through hyperedge HE-5 (induced by source paper "parallel medical imaging a new data knowledge driven evolutionary framework for medical image analysis"):
  with members:
    - "melanogans high resolution skin lesion synthesis with gans"
    - "deeplung deep 3d dual path nets for automated pulmonary nodule detection and classification"
    - "icadx interpretable computer aided diagnosis of breast masses"
    - "gan based synthetic medical image augmentation for increased cnn performance in liver lesion classification"
    - "what do we need to build explainable ai systems for the medical domain"
    - "image to image translation with conditional adversarial networks"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"

Connected through hyperedge HE-6 (induced by source paper "examining the capability of gans to replace real biomedical images in classification models training"):
  with members:
    - "generative adversarial network in medical imaging a review"
    - "predicting breast tumor proliferation from whole slide images the tupac16 challenge"
    - "progressive growing of gans for improved quality stability and variation"
    - "a survey on deep learning in medical image analysis"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"

Connected through hyperedge HE-7 (induced by source paper "generating diffusion mri scalar maps from t1 weighted images using generative adversarial networks"):
  with members:
    - "synergistic reconstruction and synthesis via generative adversarial networks for accelerated multi contrast mri"
    - "generative adversarial networks for image to image translation on multi contrast mr images a comparison of cyclegan and unit"
    - "distribution matching losses can hallucinate features in medical image translation"
    - "generative adversarial training for mra image synthesis using multi contrast mri"
    - "image synthesis in multi contrast mri with conditional generative adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "deep mr to ct synthesis using unpaired data"
    - "medical image synthesis with context aware generative adversarial networks"

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)