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: "lesion border detection in dermoscopy images"
  Abstract: Abstract   Background  Dermoscopy is one of the major imaging modalities used in the diagnosis of melanoma and other pigmented skin lesions. Due to the difficulty and subjectivity of human interpretation, computerized analysis of dermoscopy images has become an important research area. One of the most important steps in dermoscopy image analysis is the automated detection of lesion borders.    Methods  In this article, we present a systematic overview of the recent border detection methods in the literature paying particular attention to computational issues and evaluation aspects.    Conclusion  Common problems with the existing approaches include the acquisition, size, and diagnostic distribution of the test image set, the evaluation of the results, and the inadequate description of the employed methods. Border determination by dermatologists appears to depend upon higher-level knowledge, therefore it is likely that the incorporation of domain knowledge in automated methods will enable them to perform better, especially in sets of images with a variety of diagnoses.

Connected through hyperedge HE-1 (induced by source paper "improving dermoscopic image segmentation with enhanced convolutional deconvolutional networks"):
  with members:
    - "supervised saliency map driven segmentation of lesions in dermoscopic images"
    - "isic 2017 skin lesion analysis towards melanoma detection"
    - "automatic skin lesion analysis using large scale dermoscopy images and deep residual networks"
    - "recod titans at isic challenge 2017"
    - "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"
    - "theano a python framework for fast computation of mathematical expressions"
    - "u net convolutional networks for biomedical image segmentation"
    - "conditional random fields as recurrent neural networks"

Connected through hyperedge HE-2 (induced by source paper "region of interest detection in dermoscopic images for natural data augmentation"):
  with members:
    - "the ham10000 dataset a large collection of multi source dermatoscopic images of common pigmented skin lesions"
    - "multi class semantic segmentation of skin lesions via fully convolutional networks"
    - "mask r cnn"
    - "ssd single shot multibox detector"
    - "speed accuracy trade offs for modern convolutional object detectors"
    - "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"
    - "segnet a deep convolutional encoder decoder architecture for robust semantic pixel wise labelling"

Connected through hyperedge HE-3 (induced by source paper "improving dermoscopic image segmentation with enhanced convolutional deconvolutional networks"):
  with members:
    - "supervised saliency map driven segmentation of lesions in dermoscopic images"
    - "isic 2017 skin lesion analysis towards melanoma detection"
    - "automatic skin lesion analysis using large scale dermoscopy images and deep residual networks"
    - "recod titans at isic challenge 2017"
    - "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"
    - "theano a python framework for fast computation of mathematical expressions"
    - "u net convolutional networks for biomedical image segmentation"
    - "conditional random fields as recurrent neural networks"

Connected through hyperedge HE-4 (induced by source paper "skin lesion analysis towards melanoma detection using deep learning network"):
  with members:
    - "ii fcn for skin lesion analysis towards melanoma detection"
    - "spatially aware melanoma segmentation using hybrid deep learning techniques"
    - "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"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-5 (induced by source paper "dense pooling layers in fully convolutional network for skin lesion segmentation"):
  with members:
    - "deep multi scale location aware 3d convolutional neural networks for automated detection of lacunes of presumed vascular origin"
    - "extraction of skin lesions from non dermoscopic images for surgical excision of melanoma"
    - "a survey on deep learning in medical image analysis"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep learning in neural networks"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"
    - "learning deconvolution network for semantic segmentation"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "dense fully convolutional network for skin lesion segmentation"):
  with members:
    - "deep multi scale location aware 3d convolutional neural networks for automated detection of lacunes of presumed vascular origin"
    - "a survey on deep learning in medical image analysis"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep learning in neural networks"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"
    - "learning deconvolution network for semantic segmentation"
    - "adam a method for stochastic optimization"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-7 (induced by source paper "skin lesion segmentation u nets versus clustering"):
  with members:
    - "improving dermoscopic image segmentation with enhanced convolutional deconvolutional networks"
    - "k means clustering and ensemble of regressions an algorithm for the isic 2017 skin lesion segmentation challenge"
    - "improving dermoscopic image segmentation with enhanced convolutional deconvolutional networks"
    - "isic 2017 skin lesion analysis towards melanoma detection"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "extraction of skin lesions from non dermoscopic images for surgical excision of melanoma"):
  with members:
    - "brain tumor segmentation with deep neural networks"
    - "deep learning in neural 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)