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: "skin lesion synthesis with generative adversarial networks"
  Abstract: Skin cancer is by far the most common type of cancer. Early detection is the key to increase the chances for successful treatment significantly. Currently, Deep Neural Networks are the state-of-the-art results on automated skin cancer classification. To push the results further, we need to address the lack of annotated data, which is expensive and require much effort from specialists. To bypass this problem, we propose using Generative Adversarial Networks for generating realistic synthetic skin lesion images. To the best of our knowledge, our results are the first to show visually-appealing synthetic images that comprise clinically-meaningful information.

Connected through hyperedge HE-1 (induced by source paper "syncgan using learnable class specific priors to generate synthetic data for improving classifier performance on cytological images"):
  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"
    - "simulating patho realistic ultrasound images using deep generative networks with adversarial learning"
    - "image to image translation with conditional adversarial networks"
    - "deep residual learning for image recognition"
    - "rethinking the inception architecture for computer vision"
    - "conditional generative adversarial nets"

Connected through hyperedge HE-2 (induced by source paper "the six fronts of the generative adversarial networks"):
  with members:
    - "generative adversarial networks a survey and taxonomy"
    - "few shot unsupervised image to image translation"
    - "semantic image synthesis with spatially adaptive normalization"
    - "cgans with projection discriminator"
    - "how good is my gan"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "pros and cons of gan evaluation measures"
    - "multimodal unsupervised image to image translation"

Connected through hyperedge HE-3 (induced by source paper "de constructing bias on skin lesion datasets"):
  with members:
    - "skin lesion analysis toward melanoma detection 2018 a challenge hosted by the international skin imaging collaboration isic"
    - "data augmentation for skin lesion analysis"
    - "data depth and design learning reliable models for melanoma screening"
    - "data depth and design learning reliable models for skin lesion analysis"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "skin lesion analysis toward melanoma detection a challenge at the 2017 international symposium on biomedical imaging isbi hosted by the international skin imaging collaboration isic"
    - "an overview of melanoma detection in dermoscopy images using image processing and machine learning"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-4 (induced by source paper "deep learning ensembles for skin lesion segmentation analysis classification recod titans at isic challenge 2018"):
  with members:
    - "the ham10000 dataset a large collection of multi source dermatoscopic images of common pigmented skin lesions"
    - "ternausnet u net with vgg11 encoder pre trained on imagenet for image segmentation"
    - "data augmentation for skin lesion analysis"
    - "knowledge transfer for melanoma screening with deep learning"
    - "data depth and design learning reliable models for skin lesion analysis"
    - "recod titans at isic challenge 2017"
    - "fusionnet a deep fully residual convolutional neural network for image segmentation in connectomics"
    - "towards automated melanoma screening proper computer vision reliable results"

Connected through hyperedge HE-5 (induced by source paper "data augmentation for skin lesion analysis"):
  with members:
    - "data depth and design learning reliable models for melanoma screening"
    - "random erasing data augmentation"
    - "knowledge transfer for melanoma screening with deep learning"
    - "automatic skin lesion analysis using large scale dermoscopy images and deep residual networks"
    - "image classification of melanoma nevus and seborrheic keratosis by deep neural network ensemble"
    - "towards automated melanoma screening proper computer vision reliable results"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "skin lesion analysis toward melanoma detection a challenge at the 2017 international symposium on biomedical imaging isbi hosted by the international skin imaging collaboration isic"

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)