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: "a fully convolutional neural network based structured prediction approach towards the retinal vessel segmentation"
  Abstract: Automatic segmentation of retinal blood vessels from fundus images plays an important role in the computer aided diagnosis of retinal diseases. The task of blood vessel segmentation is challenging due to the extreme variations in morphology of the vessels against noisy background. In this paper, we formulate the segmentation task as a multi-label inference task and utilize the implicit advantages of the combination of convolutional neural networks and structured prediction. Our proposed convolutional neural network based model achieves strong performance and significantly outperforms the state-of-the-art for automatic retinal blood vessel segmentation on DRIVE dataset with 95.33% accuracy and 0.974 AUC score.

Connected through hyperedge HE-1 (induced by source paper "semantic transferable weakly supervised endoscopic lesions segmentation"):
  with members:
    - "learning to adapt structured output space for semantic segmentation"
    - "learning a discriminative feature network for semantic segmentation"
    - "weakly supervised deep learning for thoracic disease classification and localization on chest x rays"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "unsupervised cross modality domain adaptation of convnets for biomedical image segmentations with adversarial loss"
    - "no more discrimination cross city adaptation of road scene segmenters"
    - "cycada cycle consistent adversarial domain adaptation"
    - "adversarial discriminative domain adaptation"

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"
    - "segmentation of optic disc fovea and retinal vasculature using a single convolutional neural network"
    - "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 "ace net biomedical image segmentation with augmented contracting and expansive paths"):
  with members:
    - "deep supervision with additional labels for retinal vessel segmentation task"
    - "learning a discriminative feature network for semantic segmentation"
    - "learning steerable filters for rotation equivariant cnns"
    - "automatic liver and tumor segmentation of ct and mri volumes using cascaded fully convolutional neural networks"
    - "multi stage multi recursive input fully convolutional networks for neuronal boundary detection"
    - "rethinking atrous convolution for semantic image segmentation"
    - "fusionnet a deep fully residual convolutional neural network for image segmentation in connectomics"
    - "the importance of skip connections in biomedical image segmentation"

Connected through hyperedge HE-4 (induced by source paper "a novel deep learning pipeline for retinal vessel detection in fluorescein angiography"):
  with members:
    - "predicting cardiovascular risk factors in retinal fundus photographs using deep learning"
    - "unet a nested u net architecture for medical image segmentation"
    - "deep vessel segmentation by learning graphical connectivity"
    - "retinal vessel segmentation in fundoscopic images with generative adversarial networks"
    - "beyond the pixel wise loss for topology aware delineation"
    - "deep retinal image understanding"
    - "ensemble of deep convolutional neural networks for learning to detect retinal vessels in fundus images"
    - "image to image translation with conditional adversarial networks"

Connected through hyperedge HE-5 (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"
    - "segmentation of optic disc fovea and retinal vasculature using a single convolutional neural network"
    - "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-6 (induced by source paper "dynamic deep networks for retinal vessel segmentation"):
  with members:
    - "dunet a deformable network for retinal vessel segmentation"
    - "retinal vessel segmentation based on conditional deep convolutional generative adversarial networks"
    - "recurrent residual convolutional neural network based on u net r2u net for medical image segmentation"
    - "laddernet multi path networks based on u net for medical image segmentation"
    - "u net convolutional networks for biomedical image segmentation"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (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"

Connected through hyperedge HE-8 (induced by source paper "retinal vessel segmentation under extreme low annotation a generative adversarial network approach"):
  with members:
    - "vqa visual question answering"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

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)