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 using ensembles of thresholding methods"
  Abstract: Background#R##N##R##N#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, automated analysis of dermoscopy images has become an important research area. Border detection is often the first step in this analysis. In many cases, the lesion can be roughly separated from the background skin using a thresholding method applied to the blue channel. However, no single thresholding method appears to be robust enough to successfully handle the wide variety of dermoscopy images encountered in clinical practice.#R##N##R##N##R##N##R##N#Methods#R##N##R##N#In this article, we present an automated method for detecting lesion borders in dermoscopy images using ensembles of thres holding methods.#R##N##R##N##R##N##R##N#Conclusion#R##N##R##N#Experiments on a difficult set of 90 images demonstrate that the proposed method is robust, fast, and accurate when compared to nine state-of-the-art methods.

Connected through hyperedge HE-1 (induced by source paper "bi directional dermoscopic feature learning and multi scale consistent decision fusion for skin lesion segmentation"):
  with members:
    - "supervised saliency map driven segmentation of lesions in dermoscopic images"
    - "feature boosting network for 3d pose estimation"
    - "ce net context encoder network for 2d medical image segmentation"
    - "semantic correlation promoted shape variant context for segmentation"
    - "et net a generic edge attention guidance network for medical image segmentation"
    - "improving dermoscopic image segmentation with enhanced convolutional deconvolutional networks"
    - "boundary aware feature propagation for scene segmentation"
    - "attention gated networks learning to leverage salient regions in medical images"

Connected through hyperedge HE-2 (induced by source paper "transformation consistent self ensembling model for semi supervised medical image segmentation"):
  with members:
    - "the liver tumor segmentation benchmark lits"
    - "not so supervised a survey of semi supervised multi instance and transfer learning in medical image analysis"
    - "unsupervised domain adaptation for automatic estimation of cardiothoracic ratio"
    - "adversarial learning for semi supervised semantic segmentation"
    - "unsupervised reverse domain adaptation for synthetic medical images via adversarial training"
    - "discriminative localization in cnns for weakly supervised segmentation of pulmonary nodules"
    - "isic 2017 skin lesion analysis towards melanoma detection"
    - "segmentation of optic disc fovea and retinal vasculature using a single convolutional neural network"

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"
    - "an overview of melanoma detection in dermoscopy images using image processing and machine learning"
    - "salient object detection a benchmark"
    - "what is a salient object a dataset and a baseline model for salient object detection"

Connected through hyperedge HE-4 (induced by source paper "semi supervised skin lesion segmentation via transformation consistent self ensembling model"):
  with members:
    - "improving dermoscopic image segmentation with enhanced convolutional deconvolutional networks"
    - "adversarial learning for semi supervised semantic segmentation"
    - "deeply supervised rotation equivariant network for lesion segmentation in dermoscopy images"
    - "improving dermoscopic image segmentation with enhanced convolutional deconvolutional networks"
    - "automatic skin lesion segmentation using semi supervised learning technique"
    - "isic 2017 skin lesion analysis towards melanoma detection"
    - "automatic skin lesion analysis using large scale dermoscopy images and deep residual networks"
    - "harmonic networks deep translation and rotation equivariance"

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"
    - "an overview of melanoma detection in dermoscopy images using image processing and machine learning"

Connected through hyperedge HE-6 (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-7 (induced by source paper "accessible melanoma detection using smartphones and mobile image analysis"):
  with members:
    - "extraction of skin lesions from non dermoscopic images for surgical excision of melanoma"
    - "confidentcare a clinical decision support system for personalized breast cancer screening"
    - "skincure an innovative smart phone based application to assist in melanoma early detection and prevention"

Connected through hyperedge HE-8 (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"
    - "an improved objective evaluation measure for 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)