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: "segmentation of optic disc fovea and retinal vasculature using a single convolutional neural network"
  Abstract: We have developed and trained a convolutional neural network to automatically and simultaneously segment optic disc, fovea and blood vessels. Fundus images were normalised before segmentation was performed to enforce consistency in background lighting and contrast. For every effective point in the fundus image, our algorithm extracted three channels of input from the neighbourhood of the point and forward the response across the 7 layer network. In average, our segmentation achieved an accuracy of 92.68 percent on the testing set from Drive database.

Connected through hyperedge HE-1 (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"
    - "weakly supervised localization of diabetic retinopathy lesions in retinal fundus images"

Connected through hyperedge HE-2 (induced by source paper "from patch to image segmentation using fully convolutional networks application to retinal images"):
  with members:
    - "joint optic disc and cup segmentation based on multi label deep network and polar transformation"
    - "convolutional neural networks for medical image analysis full training or fine tuning"
    - "a survey on deep learning in medical image analysis"
    - "a fully convolutional neural network based structured prediction approach towards the retinal vessel segmentation"
    - "densely connected convolutional networks"
    - "holistically nested edge detection"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "dunet a deformable network for retinal vessel segmentation"):
  with members:
    - "recurrent residual convolutional neural network based on u net r2u net for medical image segmentation"
    - "a labeling free approach to supervising deep neural networks for retinal blood vessel segmentation"
    - "a fully convolutional neural network based structured prediction approach towards the retinal vessel segmentation"
    - "ensemble of deep convolutional neural networks for learning to detect retinal vessels in fundus images"
    - "u net convolutional networks for biomedical image segmentation"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "retinal vessel segmentation using the 2 d gabor wavelet and supervised classification"

Connected through hyperedge HE-4 (induced by source paper "the history began from alexnet a comprehensive survey on deep learning approaches"):
  with members:
    - "hyperspectral image classification with markov random fields and a convolutional neural network"
    - "an analysis of deep neural network models for practical applications"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "end to end unsupervised deformable image registration with a convolutional neural network"
    - "mdnet a semantically and visually interpretable medical image diagnosis network"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "a deep convolutional neural network for background subtraction"
    - "a survey of neuromorphic computing and neural networks in hardware"

Connected through hyperedge HE-5 (induced by source paper "deep learning based computer aided diagnosis systems for diabetic retinopathy a survey"):
  with members:
    - "joint optic disc and cup segmentation based on multi label deep network and polar transformation"
    - "gan based synthetic medical image augmentation for increased cnn performance in liver lesion classification"
    - "deep image mining for diabetic retinopathy screening"
    - "convolutional neural networks for medical image analysis full training or fine tuning"
    - "show and tell lessons learned from the 2015 mscoco image captioning challenge"
    - "optic disc and cup segmentation methods for glaucoma detection with modification of u net convolutional neural network"
    - "a survey on deep learning in medical image analysis"
    - "zoom in net deep mining lesions for diabetic retinopathy detection"

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)