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: "improved microaneurysm detection using deep neural networks"
  Abstract: In this work, we propose a novel microaneurysm (MA) detection for early diabetic retinopathy screening using color fundus images. Since MA usually the first lesions to appear as an indicator of diabetic retinopathy, accurate detection of MA is necessary for treatment. Each pixel of the image is classified as either MA or non-MA using a deep neural network with dropout training procedure using maxout activation function. No preprocessing step or manual feature extraction is required. Substantial improvements over standard MA detection method based on the pipeline of preprocessing, feature extraction, classification followed by post processing is achieved. The presented method is evaluated in publicly available Retinopathy Online Challenge (ROC) and Diaretdb1v2 database and achieved state-of-the-art accuracy.

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 "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-3 (induced by source paper "sparsely activated networks a new method for decomposing and compressing data"):
  with members:
    - "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"
    - "brain tumor type classification via capsule networks"
    - "automatic calcium scoring in low dose chest ct using deep neural networks with dilated convolutions"
    - "medical image analysis using convolutional neural networks a review"
    - "beat by beat classifying cardiac arrhythmias with recurrent neural networks"
    - "recognizing abnormal heart sounds using deep learning"

Connected through hyperedge HE-4 (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-5 (induced by source paper "towards ophthalmologist level accurate deep learning system for oct screening and diagnosis"):
  with members:
    - "towards radiologist level accurate deep learning system for pulmonary screening"
    - "deep learning based retinal oct segmentation"
    - "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases"
    - "rethinking convolutional semantic segmentation learning"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "traffic sign classification using deep inception based convolutional networks"
    - "a gaussian scale space approach for exudates detection classification and severity prediction"

Connected through hyperedge HE-6 (induced by source paper "retinal microaneurysms detection using local convergence index features"):
  with members:
    - "an ensemble based system for microaneurysm detection and diabetic retinopathy grading"

Connected through hyperedge HE-7 (induced by source paper "rethinking convolutional semantic segmentation learning"):
  with members:
    - "large kernel matters improve semantic segmentation by global convolutional network"
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "deep learning generalization requires deep compositional feature space design"
    - "mask r cnn"
    - "refinenet multi path refinement networks for high resolution semantic segmentation"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "pyramid scene parsing network"
    - "wider or deeper revisiting the resnet model for visual recognition"

Connected through hyperedge HE-8 (induced by source paper "deep image mining for diabetic retinopathy screening"):
  with members:
    - "ensemble of deep convolutional neural networks for learning to detect retinal vessels in fundus images"
    - "understanding neural networks through deep visualization"
    - "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"

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)