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: "convolutional neural network committees for melanoma classification with classical and expert knowledge based image transforms data augmentation"
  Abstract: Skin cancer is a major public health problem, as is the most common type of cancer and represents more than half of cancer diagnoses worldwide. Early detection influences the outcome of the disease and motivates our work. We investigate the composition of CNN committees and data augmentation for the the ISBI 2017 Melanoma Classification Challenge (named Skin Lesion Analysis towards Melanoma Detection) facing the peculiarities of dealing with such a small, unbalanced, biological database. For that, we explore committees of Convolutional Neural Networks trained over the ISBI challenge training dataset artificially augmented by both classical image processing transforms and image warping guided by specialist knowledge about the lesion axis and improve the final classifier invariance to common melanoma variations.

Connected through hyperedge HE-1 (induced by source paper "domain adaptation for holistic skin detection"):
  with members:
    - "segmentation of liver lesions with reduced complexity deep models"
    - "domain adaptation for visual applications a comprehensive survey"
    - "adversarial discriminative domain adaptation"
    - "the effectiveness of data augmentation in image classification using deep learning"
    - "unsupervised pixel level domain adaptation with generative adversarial networks"
    - "improved relation classification by deep recurrent neural networks with data augmentation"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"
    - "unsupervised domain adaptation in brain lesion segmentation with adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "towards automated melanoma detection with deep learning data purification and augmentation"):
  with members:
    - "the ham10000 dataset a large collection of multi source dermatoscopic images of common pigmented skin lesions"
    - "knowledge transfer for melanoma screening with deep learning"
    - "skin lesion analysis towards melanoma detection using deep learning network"
    - "image classification of melanoma nevus and seborrheic keratosis by deep neural network ensemble"
    - "recod titans at isic challenge 2017"
    - "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"
    - "improved techniques for training gans"

Connected through hyperedge HE-3 (induced by source paper "data depth and design learning reliable models for skin lesion analysis"):
  with members:
    - "data augmentation for skin lesion analysis"
    - "skin lesion classification using deep multi scale convolutional neural networks"
    - "skin lesion detection based on an ensemble of deep convolutional neural network"
    - "knowledge transfer for melanoma screening with deep learning"
    - "skin lesion classification using class activation map"
    - "a novel multi task deep learning model for skin lesion segmentation and classification"
    - "incorporating the knowledge of dermatologists to convolutional neural networks for the diagnosis of skin lesions"
    - "a survey on deep learning in medical image analysis"

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)