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: "melanogans high resolution skin lesion synthesis with gans"
  Abstract: Generative Adversarial Networks (GANs) have been successfully used to synthesize realistically looking images of faces, scenery and even medical images. Unfortunately, they usually require large training datasets, which are often scarce in the medical field, and to the best of our knowledge GANs have been only applied for medical image synthesis at fairly low resolution. However, many state-of-the-art machine learning models operate on high resolution data as such data carries indispensable, valuable information. In this work, we try to generate realistically looking high resolution images of skin lesions with GANs, using only a small training dataset of 2000 samples. The nature of the data allows us to do a direct comparison between the image statistics of the generated samples and the real dataset. We both quantitatively and qualitatively compare state-of-the-art GAN architectures such as DCGAN and LAPGAN against a modification of the latter for the task of image generation at a resolution of 256x256px. Our investigation shows that we can approximate the real data distribution with all of the models, but we notice major differences when visually rating sample realism, diversity and artifacts. In a set of use-case experiments on skin lesion classification, we further show that we can successfully tackle the problem of heavy class imbalance with the help of synthesized high resolution melanoma samples.

Connected through hyperedge HE-1 (induced by source paper "dermgan synthetic generation of clinical skin images with pathology"):
  with members:
    - "semantic image synthesis with spatially adaptive normalization"
    - "generative image translation for data augmentation of bone lesion pathology"
    - "efficient gan based anomaly detection"
    - "the ham10000 dataset a large collection of multi source dermatoscopic images of common pigmented skin lesions"
    - "conditional image to image translation"
    - "large scale gan training for high fidelity natural image synthesis"
    - "exploring the limits of weakly supervised pretraining"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-2 (induced by source paper "ct gan malicious tampering of 3d medical imagery using deep learning"):
  with members:
    - "faceforensics learning to detect manipulated facial images"
    - "generative adversarial network in medical imaging a review"
    - "fighting fake news image splice detection via learned self consistency"
    - "cross modality synthesis from ct to pet using fcn and gan networks for improved automated lesion detection"
    - "forensictransfer weakly supervised domain adaptation for forgery detection"
    - "noiseprint a cnn based camera model fingerprint"
    - "learning implicit brain mri manifolds with deep learning"
    - "blood vessel geometry synthesis using generative adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "parallel medical imaging a new data knowledge driven evolutionary framework for medical image analysis"):
  with members:
    - "deeplung deep 3d dual path nets for automated pulmonary nodule detection and classification"
    - "icadx interpretable computer aided diagnosis of breast masses"
    - "gans for medical image analysis"
    - "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-4 (induced by source paper "scargan chained generative adversarial networks to simulate pathological tissue on cardiovascular mr scans"):
  with members:
    - "gan based synthetic medical image augmentation for increased cnn performance in liver lesion classification"
    - "progressive growing of gans for improved quality stability and variation"
    - "how to fool radiologists with generative adversarial networks a visual turing test for lung cancer diagnosis"
    - "fastventricle cardiac segmentation with enet"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved training of wasserstein gans"
    - "deep mr to ct synthesis using unpaired data"
    - "rendergan generating realistic labeled data"

Connected through hyperedge HE-5 (induced by source paper "imagining an engineer on gan based data augmentation perpetuating biases"):
  with members:
    - "synthetic data augmentation using gan for improved liver lesion classification"
    - "doping generative data augmentation for unsupervised anomaly detection with gan"
    - "generating multi label discrete patient records using generative adversarial networks"
    - "unsupervised representation learning with deep convolutional 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)