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: "an overview of melanoma detection in dermoscopy images using image processing and machine learning"
  Abstract: The incidence of malignant melanoma continues to increase worldwide. This cancer can strike at any age; it is one of the leading causes of loss of life in young persons. Since this cancer is visible on the skin, it is potentially detectable at a very early stage when it is curable. New developments have converged to make fully automatic early melanoma detection a real possibility. First, the advent of dermoscopy has enabled a dramatic boost in clinical diagnostic ability to the point that melanoma can be detected in the clinic at the very earliest stages. The global adoption of this technology has allowed accumulation of large collections of dermoscopy images of melanomas and benign lesions validated by histopathology. The development of advanced technologies in the areas of image processing and machine learning have given us the ability to allow distinction of malignant melanoma from the many benign mimics that require no biopsy. These new technologies should allow not only earlier detection of melanoma, but also reduction of the large number of needless and costly biopsy procedures. Although some of the new systems reported for these technologies have shown promise in preliminary trials, widespread implementation must await further technical progress in accuracy and reproducibility. In this paper, we provide an overview of computerized detection of melanoma in dermoscopy images. First, we discuss the various aspects of lesion segmentation. Then, we provide a brief overview of clinical feature segmentation. Finally, we discuss the classification stage where machine learning algorithms are applied to the attributes generated from the segmented features to predict the existence of melanoma.

Connected through hyperedge HE-1 (induced by source paper "visual diagnosis of dermatological disorders human and machine performance"):
  with members:
    - "skin lesion analysis toward melanoma detection 2018 a challenge hosted by the international skin imaging collaboration isic"
    - "diagnostic accuracy of content based dermatoscopic image retrieval with deep classification features"
    - "the ham10000 dataset a large collection of multi source dermatoscopic images of common pigmented skin lesions"
    - "knowledge transfer for melanoma screening with deep learning"
    - "deep learning ensembles for melanoma recognition in dermoscopy images"
    - "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"
    - "compact bilinear pooling"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-2 (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"
    - "skin lesion synthesis with generative adversarial networks"
    - "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"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-3 (induced by source paper "supervised saliency map driven segmentation of lesions in dermoscopic images"):
  with members:
    - "a survey on deep learning in medical image analysis"
    - "salient object detection a discriminative regional feature integration approach"
    - "deep learning ensembles for melanoma recognition in dermoscopy images"
    - "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"
    - "salient object detection a benchmark"
    - "what is a salient object a dataset and a baseline model for salient object detection"
    - "lesion border detection in dermoscopy images using ensembles of thresholding methods"

Connected through hyperedge HE-4 (induced by source paper "unsupervised and semi supervised learning with categorical generative adversarial networks assisted by wasserstein distance for dermoscopy image classification"):
  with members:
    - "towards automatic pulmonary nodule management in lung cancer screening with deep 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"
    - "regularization with stochastic transformations and perturbations for deep semi supervised learning"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "semi supervised learning with ladder networks"

Connected through hyperedge HE-5 (induced by source paper "melanoma recognition with an ensemble of techniques for segmentation and a structural analysis for classification"):
  with members:
    - "the ham10000 dataset a large collection of multi source dermatoscopic images of common pigmented skin lesions"
    - "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"
    - "lesion border detection in dermoscopy images using ensembles of thresholding methods"

Connected through hyperedge HE-6 (induced by source paper "deep learning ensembles for melanoma recognition in dermoscopy images"):
  with members:
    - "towards automated melanoma screening proper computer vision reliable results"
    - "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"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "facenet a unified embedding for face recognition and clustering"
    - "caffe convolutional architecture for fast feature embedding"
    - "sparse modeling for image and vision processing"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-7 (induced by source paper "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"):
  with members:
    - "improving dermoscopic image segmentation with enhanced convolutional deconvolutional networks"
    - "improving dermoscopic image segmentation with enhanced convolutional deconvolutional networks"
    - "incorporating the knowledge of dermatologists to convolutional neural networks for the diagnosis of skin lesions"
    - "skin lesion analysis towards melanoma detection using deep learning network"
    - "recod titans at isic challenge 2017"
    - "deep learning ensembles for melanoma recognition in dermoscopy images"
    - "lesion border detection in dermoscopy images"
    - "automatic detection of blue white veil and related structures in dermoscopy images"

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)