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: "automatic detection of blue white veil and related structures in dermoscopy images"
  Abstract: Dermoscopy is a non-invasive skin imaging technique, which permits visualization of features of pigmented melanocytic neoplasms that are not discernable by examination with the naked eye. One of the most important features for the diagnosis of melanoma in dermoscopy images is the blue-white veil (irregular, structureless areas of confluent blue pigmentation with an overlying white “ground-glass” film). In this article, we present a machine learning approach to the detection of blue-white veil and related structures in dermoscopy images. The method involves contextual pixel classification using a decision tree classifier. The percentage of blue-white areas detected in a lesion combined with a simple shape descriptor yielded a sensitivity of 69.35% and a specificity of 89.97% on a set of 545 dermoscopy images. The sensitivity rises to 78.20% for detection of blue veil in those cases where it is a primary feature for melanoma recognition.

Connected through hyperedge HE-1 (induced by source paper "slsdeep skin lesion segmentation based on dilated residual and pyramid pooling networks"):
  with members:
    - "improving dermoscopic image segmentation with enhanced convolutional deconvolutional networks"
    - "skin lesion segmentation u nets versus clustering"
    - "dilated residual networks"
    - "improving dermoscopic image segmentation with enhanced convolutional deconvolutional networks"
    - "isic 2017 skin lesion analysis towards melanoma detection"
    - "automatic skin lesion analysis using large scale dermoscopy images and deep residual networks"
    - "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"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"

Connected through hyperedge HE-2 (induced by source paper "improving automatic skin lesion segmentation using adversarial learning based data augmentation"):
  with members:
    - "the ham10000 dataset a large collection of multi source dermatoscopic images of common pigmented skin lesions"
    - "refinenet multi path refinement networks for high resolution semantic segmentation"
    - "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"
    - "image to image translation with conditional adversarial networks"
    - "wider or deeper revisiting the resnet model for visual recognition"
    - "deep residual learning for image recognition"
    - "conditional generative adversarial nets"

Connected through hyperedge HE-3 (induced by source paper "fully convolutional networks to detect clinical dermoscopic features"):
  with members:
    - "skin lesion analysis towards melanoma detection using deep learning network"
    - "deep learning in medical image analysis and multimodal learning for clinical decision support"
    - "deep learning ensembles for melanoma recognition in dermoscopy images"
    - "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"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-4 (induced by source paper "automatic skin lesion analysis using large scale dermoscopy images and deep residual networks"):
  with members:
    - "residual networks behave like ensembles of relatively shallow networks"
    - "wide residual networks"
    - "wider or deeper revisiting the resnet model for visual recognition"
    - "deep residual learning for image recognition"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-5 (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"
    - "an overview of melanoma detection in dermoscopy images using image processing and machine learning"
    - "lesion border detection in dermoscopy images"

Connected through hyperedge HE-6 (induced by source paper "an overview of melanoma detection in dermoscopy images using image processing and machine learning"):
  with members:
    - "lesion border detection in dermoscopy images using ensembles of thresholding methods"
    - "lesion border detection in dermoscopy images"
    - "an improved objective evaluation measure for border detection in dermoscopy images"

Connected through hyperedge HE-7 (induced by source paper "learning to detect blue white structures in dermoscopy images with weak supervision"):
  with members:
    - "multiple instance learning by discriminative training of markov networks"

Connected through hyperedge HE-8 (induced by source paper "an improved objective evaluation measure for border detection in dermoscopy images"):
  with members:
    - "lesion border detection 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)