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 segmentation using semi supervised learning technique"
  Abstract: Skin cancer is the most common of all cancers and each year million cases of skin cancer are treated. Treating and curing skin cancer is easy, if it is diagnosed and treated at an early stage. In this work we propose an automatic technique for skin lesion segmentation in dermoscopic images which helps in classifying the skin cancer types. The proposed method comprises of two major phases (1) preprocessing and (2) segmentation using semi-supervised learning algorithm. In the preprocessing phase noise are removed using filtering technique and in the segmentation phase skin lesions are segmented based on clustering technique. K-means clustering algorithm is used to cluster the preprocessed images and skin lesions are filtered from these clusters based on the color feature. Color of the skin lesions are learned from the training images using histograms calculations in RGB color space. The training images were downloaded from the ISIC 2017 challenge website and the experimental results were evaluated using validation and test sets.

Connected through hyperedge HE-1 (induced by source paper "self learning ai framework for skin lesion image segmentation and classification"):
  with members:
    - "skin lesion analysis toward melanoma detection 2018 a challenge hosted by the international skin imaging collaboration isic"
    - "the ham10000 dataset a large collection of multi source dermatoscopic images of common pigmented skin lesions"
    - "self learning to detect and segment cysts in lung ct images without manual annotation"
    - "skin lesion segmentation u nets versus clustering"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-2 (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"
    - "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"
    - "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"

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)