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 skin lesion analysis using large scale dermoscopy images and deep residual networks"
  Abstract: Malignant melanoma has one of the most rapidly increasing incidences in the world and has a considerable mortality rate. Early diagnosis is particularly important since melanoma can be cured with prompt excision. Dermoscopy images play an important role in the non-invasive early detection of melanoma [1]. However, melanoma detection using human vision alone can be subjective, inaccurate and poorly reproducible even among experienced dermatologists. This is attributed to the challenges in interpreting images with diverse characteristics including lesions of varying sizes and shapes, lesions that may have fuzzy boundaries, different skin colors and the presence of hair [2]. Therefore, the automatic analysis of dermoscopy images is a valuable aid for clinical decision making and for image-based diagnosis to identify diseases such as melanoma [1-4]. Deep residual networks (ResNets) has achieved state-of-the-art results in image classification and detection related problems [5-8]. In this ISIC 2017 skin lesion analysis challenge [9], we propose to exploit the deep ResNets for robust visual features learning and representations.

Connected through hyperedge HE-1 (induced by source paper "solo or ensemble choosing a cnn architecture for melanoma classification"):
  with members:
    - "skin lesion analysis toward melanoma detection 2018 a challenge hosted by the international skin imaging collaboration isic"
    - "rethinking imagenet pre training"
    - "do better imagenet models transfer better"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "skin lesion analysis towards melanoma detection via end to end deep learning of convolutional neural networks"
    - "ensemble of convolutional neural networks for dermoscopic images classification"
    - "skin lesion diagnosis using ensembles unscaled multi crop evaluation and loss weighting"
    - "data augmentation for skin lesion analysis"

Connected through hyperedge HE-2 (induced by source paper "a mutual bootstrapping model for automated skin lesion segmentation and classification"):
  with members:
    - "skin lesion classification using cnns with patch based attention and diagnosis guided loss weighting"
    - "improving dermoscopic image segmentation with enhanced convolutional deconvolutional networks"
    - "star shape prior in fully convolutional networks for skin lesion segmentation"
    - "slsdeep skin lesion segmentation based on dilated residual and pyramid pooling networks"
    - "revisiting dilated convolution a simple approach for weakly and semi supervised semantic segmentation"
    - "focal loss for dense object detection"
    - "object region mining with adversarial erasing a simple classification to semantic segmentation approach"
    - "dilated residual networks"

Connected through hyperedge HE-3 (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-4 (induced by source paper "convolutional sparse kernel network for unsupervised medical image analysis"):
  with members:
    - "convolutional neural networks for medical image analysis full training or fine tuning"
    - "3d semantic segmentation with submanifold sparse convolutional networks"
    - "a survey on deep learning in medical image analysis"
    - "deep convolutional neural networks for computer aided detection cnn architectures dataset characteristics and transfer 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"
    - "weight normalization a simple reparameterization to accelerate training of deep neural networks"
    - "unsupervised deep feature extraction for remote sensing image classification"
    - "all you need is a good init"

Connected through hyperedge HE-5 (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"
    - "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"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-6 (induced by source paper "an active learning approach for reducing annotation cost in skin lesion analysis"):
  with members:
    - "robust learning at noisy labeled medical images applied to skin lesion classification"
    - "efficient active learning for image classification and segmentation using a sample selection and conditional generative adversarial network"
    - "suggestive annotation a deep active learning framework for biomedical image segmentation"
    - "mixup beyond empirical risk minimization"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "segmentation of skin lesions and their attributes using multi scale convolutional neural networks and domain specific augmentations"):
  with members:
    - "supervised saliency map driven segmentation of lesions in dermoscopic images"
    - "on the convergence of adam and beyond"
    - "improving dermoscopic image segmentation with enhanced convolutional deconvolutional networks"
    - "a detection and segmentation architecture for skin lesion segmentation on dermoscopy images"
    - "automatic skin lesion segmentation using deep fully convolutional networks"
    - "the ham10000 dataset a large collection of multi source dermatoscopic images of common pigmented skin lesions"
    - "semi supervised skin lesion segmentation via transformation consistent self ensembling model"
    - "slsdeep skin lesion segmentation based on dilated residual and pyramid pooling networks"

Connected through hyperedge HE-8 (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"
    - "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"
    - "pyramid scene parsing network"

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)