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: "spatially aware melanoma segmentation using hybrid deep learning techniques"
  Abstract: In this paper, we proposed using a hybrid method that utilises deep convolutional and recurrent neural networks for accurate delineation of skin lesion of images supplied with ISBI 2017 lesion segmentation challenge. The proposed method was trained using 1800 images and tested on 150 images from ISBI 2017 challenge.

Connected through hyperedge HE-1 (induced by source paper "focusnet an attention based fully convolutional network for medical image segmentation"):
  with members:
    - "recurrent residual convolutional neural network based on u net r2u net for medical image segmentation"
    - "ii fcn for skin lesion analysis towards melanoma detection"
    - "skin lesion analysis towards melanoma detection using deep learning network"
    - "identity mappings in deep residual networks"
    - "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"
    - "lip reading sentences in the wild"
    - "you only look once unified real time object detection"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-2 (induced by source paper "the effects of image pre and post processing wavelet decomposition and local binary patterns on u nets for skin lesion segmentation"):
  with members:
    - "improving dermoscopic image segmentation with enhanced convolutional deconvolutional networks"
    - "skin lesion segmentation u nets versus clustering"
    - "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"
    - "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"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-3 (induced by source paper "skinnet a deep learning framework for skin lesion segmentation"):
  with members:
    - "skin lesion analysis towards melanoma detection using deep learning network"
    - "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"
    - "multi scale context aggregation by dilated convolutions"
    - "adam a method for stochastic optimization"

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"
    - "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"
    - "rethinking the inception architecture for computer vision"

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)