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: "ensemble of deep convolutional neural networks for learning to detect retinal vessels in fundus images"
  Abstract: Vision impairment due to pathological damage of the retina can largely be prevented through periodic screening using fundus color imaging. However the challenge with large scale screening is the inability to exhaustively detect fine blood vessels crucial to disease diagnosis. In this work we present a computational imaging framework using deep and ensemble learning for reliable detection of blood vessels in fundus color images. An ensemble of deep convolutional neural networks is trained to segment vessel and non-vessel areas of a color fundus image. During inference, the responses of the individual ConvNets of the ensemble are averaged to form the final segmentation. In experimental evaluation with the DRIVE database, we achieve the objective of vessel detection with maximum average accuracy of 94.7\% and area under ROC curve of 0.9283.

Connected through hyperedge HE-1 (induced by source paper "ophthalmic diagnosis using deep learning with fundus images a critical review"):
  with members:
    - "pixelbnn augmenting the pixelcnn with batch normalization and the presentation of a fast architecture for retinal vessel segmentation"
    - "embedded deep learning in ophthalmology making ophthalmic imaging smarter"
    - "classification of findings with localized lesions in fundoscopic images using a regionally guided cnn"
    - "deep supervision with additional labels for retinal vessel segmentation task"
    - "deep visual domain adaptation a survey"
    - "predicting optical coherence tomography derived diabetic macular edema grades from fundus photographs using deep learning"
    - "deep learning based retinal oct segmentation"
    - "disc aware ensemble network for glaucoma screening from fundus image"

Connected through hyperedge HE-2 (induced by source paper "ensemble of deep convolutional neural networks for automatic pavement crack detection and measurement"):
  with members:
    - "automatic pavement crack detection based on structured prediction with the convolutional neural network"
    - "u net convolutional networks for biomedical image segmentation"
    - "very deep convolutional networks for large scale image recognition"
    - "striving for simplicity the all convolutional net"

Connected through hyperedge HE-3 (induced by source paper "deep learning in cardiology"):
  with members:
    - "pixelbnn augmenting the pixelcnn with batch normalization and the presentation of a fast architecture for retinal vessel segmentation"
    - "estimation of the volume of the left ventricle from mri images using deep neural networks"
    - "deep learning analysis of the myocardium in coronary ct angiography for identification of patients with functionally significant coronary artery stenosis"
    - "convolutional capsule network for classification of breast cancer histology images"
    - "predicting cardiovascular risk factors in retinal fundus photographs using deep learning"
    - "anatomically constrained neural networks acnns application to cardiac image enhancement and segmentation"
    - "brain tumor type classification via capsule networks"
    - "deepheart semi supervised sequence learning for cardiovascular risk prediction"

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"
    - "a fully convolutional neural network based structured prediction approach towards the retinal vessel segmentation"
    - "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"
    - "a fully convolutional neural network based structured prediction approach towards the retinal vessel segmentation"
    - "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 "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-7 (induced by source paper "deep image mining for diabetic retinopathy screening"):
  with members:
    - "understanding neural networks through deep visualization"
    - "improved microaneurysm detection using deep neural networks"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "adam a method for stochastic optimization"
    - "imagenet large scale visual recognition challenge"
    - "fractional max pooling"
    - "visualizing and understanding convolutional networks"
    - "deep inside convolutional networks visualising image classification models and saliency maps"

Connected through hyperedge HE-8 (induced by source paper "a fully convolutional neural network based structured prediction approach towards the retinal vessel segmentation"):
  with members:
    - "deep neural ensemble for retinal vessel segmentation in fundus images towards achieving label free angiography"
    - "retinal vessel segmentation using the 2 d gabor wavelet and supervised classification"

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.LG(Machine Learning)