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: "retinal vessel segmentation using the 2 d gabor wavelet and supervised classification"
  Abstract: We present a method for automated segmentation of the vasculature in retinal images. The method produces segmentations by classifying each image pixel as vessel or nonvessel, based on the pixel's feature vector. Feature vectors are composed of the pixel's intensity and two-dimensional Gabor wavelet transform responses taken at multiple scales. The Gabor wavelet is capable of tuning to specific frequencies, thus allowing noise filtering and vessel enhancement in a single step. We use a Bayesian classifier with class-conditional probability density functions (likelihoods) described as Gaussian mixtures, yielding a fast classification, while being able to model complex decision surfaces. The probability distributions are estimated based on a training set of labeled pixels obtained from manual segmentations. The method's performance is evaluated on publicly available DRIVE (Staal et al.,2004) and STARE (Hoover et al.,2000) databases of manually labeled images. On the DRIVE database, it achieves an area under the receiver operating characteristic curve of 0.9614, being slightly superior than that presented by state-of-the-art approaches. We are making our implementation available as open source MATLAB scripts for researchers interested in implementation details, evaluation, or development of methods

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 "automatic catheter detection in pediatric x ray images using a scale recurrent network and synthetic data"):
  with members:
    - "scale recurrent network for deep image deblurring"
    - "sharpness aware low dose ct denoising using conditional generative adversarial network"
    - "fully automatic and real time catheter segmentation in x ray fluoroscopy"
    - "scan structure correcting adversarial network for organ segmentation in chest x rays"
    - "deep residual learning for image recognition"
    - "convolutional lstm network a machine learning approach for precipitation nowcasting"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-3 (induced by source paper "learning likelihoods with conditional normalizing flows"):
  with members:
    - "integer discrete flows and lossless compression"
    - "emerging convolutions for generative normalizing flows"
    - "guided image generation with conditional invertible neural networks"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "unsupervised image super resolution using cycle in cycle generative adversarial networks"
    - "ffjord free form continuous dynamics for scalable reversible generative models"
    - "learning to maintain natural image statistics"
    - "pixelcnn improving the pixelcnn with discretized logistic mixture likelihood and other modifications"

Connected through hyperedge HE-4 (induced by source paper "deep distance transform for tubular structure segmentation in ct scans"):
  with members:
    - "one network to segment them all a general lightweight system for accurate 3d medical image segmentation"
    - "a large annotated medical image dataset for the development and evaluation of segmentation algorithms"
    - "nnu net breaking the spell on successful medical image segmentation"
    - "abdominal multi organ segmentation with organ attention networks and statistical fusion"
    - "kid net convolution networks for kidney vessels segmentation from ct volumes"
    - "deepflux for skeletons in the wild"
    - "3d semi supervised learning with uncertainty aware multi view co training"
    - "training multi organ segmentation networks with sample selection by relaxed upper confident bound"

Connected through hyperedge HE-5 (induced by source paper "connection sensitive attention u net for accurate retinal vessel segmentation"):
  with members:
    - "attention u net learning where to look for the pancreas"
    - "retinal vessel segmentation in fundoscopic images with generative adversarial networks"
    - "decoupled weight decay regularization"
    - "deepunet a deep fully convolutional network for pixel level sea land segmentation"
    - "iterative deep learning for network topology extraction"
    - "beyond the pixel wise loss for topology aware delineation"
    - "deep retinal image understanding"
    - "holistically nested edge detection"

Connected through hyperedge HE-6 (induced by source paper "pixelbnn augmenting the pixelcnn with batch normalization and the presentation of a fast architecture for retinal vessel segmentation"):
  with members:
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "conditional image generation with pixelcnn decoders"
    - "pixel recurrent neural networks"
    - "video pixel networks"
    - "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"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (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"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "bts dsn deeply supervised neural network with short connections for retinal vessel segmentation"):
  with members:
    - "deep supervision with additional labels for retinal vessel segmentation task"
    - "structure measure a new way to evaluate foreground maps"
    - "deep retinal image understanding"
    - "holistically nested edge detection"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"
    - "caffe convolutional architecture for fast feature embedding"
    - "fully convolutional networks for semantic segmentation"

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)