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: "deep image mining for diabetic retinopathy screening"
  Abstract: Abstract   Deep learning is quickly becoming the leading methodology for medical image analysis. Given a large medical archive, where each image is associated with a diagnosis, efficient pathology detectors or classifiers can be trained with virtually no expert knowledge about the target pathologies. However, deep learning algorithms, including the popular ConvNets, are black boxes: little is known about the local patterns analyzed by ConvNets to make a decision at the image level. A solution is proposed in this paper to create heatmaps showing which pixels in images play a role in the image-level predictions. In other words, a ConvNet trained for image-level classification can be used to detect lesions as well. A generalization of the backpropagation method is proposed in order to train ConvNets that produce high-quality heatmaps. The proposed solution is applied to diabetic retinopathy (DR) screening in a dataset of almost 90,000 fundus photographs from the 2015 Kaggle Diabetic Retinopathy competition and a private dataset of almost 110,000 photographs (e-ophtha). For the task of detecting referable DR, very good detection performance was achieved:      A  z   =  0.954     in Kaggle’s dataset and      A  z   =  0.949     in e-ophtha. Performance was also evaluated at the image level and at the lesion level in the DiaretDB1 dataset, where four types of lesions are manually segmented: microaneurysms, hemorrhages, exudates and cotton-wool spots. For the task of detecting images containing these four lesion types, the proposed detector, which was trained to detect referable DR, outperforms recent algorithms trained to detect those lesions specifically, with pixel-level supervision. At the lesion level, the proposed detector outperforms heatmap generation algorithms for ConvNets. This detector is part of the Messidor® system for mobile eye pathology screening. Because it does not rely on expert knowledge or manual segmentation for detecting relevant patterns, the proposed solution is a promising image mining tool, which has the potential to discover new biomarkers in images.

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 detection of rare pathologies in fundus photographs using few shot learning"):
  with members:
    - "cats or cat scans transfer learning from natural or medical image source data sets"
    - "generalizing from a few examples a survey on few shot learning"
    - "multi task learning for chest x ray abnormality classification on noisy labels"
    - "learning transferable architectures for scalable image recognition"
    - "prototypical networks for few shot learning"
    - "learning to compare relation network for few shot learning"
    - "a survey on deep learning in medical image analysis"
    - "attentive recurrent comparators"

Connected through hyperedge HE-3 (induced by source paper "certifiably robust interpretation in deep learning"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "towards robust locally linear deep networks"
    - "provably robust deep learning via adversarially trained smoothed classifiers"
    - "generalizing multistain immunohistochemistry tissue segmentation using one shot color deconvolution deep neural networks"
    - "decoupling direction and norm for efficient gradient based l2 adversarial attacks and defenses"
    - "certified adversarial robustness with additive noise"
    - "sanity checks for saliency maps"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"

Connected through hyperedge HE-4 (induced by source paper "iterative augmentation of visual evidence for weakly supervised lesion localization in deep interpretability frameworks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "deepseenet a deep learning model for automated classification of patient based age related macular degeneration severity from color fundus photographs"
    - "producing radiologist quality reports for interpretable artificial intelligence"
    - "icadx interpretable computer aided diagnosis of breast masses"
    - "generative image inpainting with contextual attention"
    - "explaining nonlinear classification decisions with deep taylor decomposition"
    - "axiomatic attribution for deep networks"
    - "towards better understanding of gradient based attribution methods for deep neural networks"

Connected through hyperedge HE-5 (induced by source paper "automatic detection of multiple pathologies in fundus photographs using spin off learning"):
  with members:
    - "cats or cat scans transfer learning from natural or medical image source data sets"
    - "multi task learning for chest x ray abnormality classification on noisy labels"
    - "learning transferable architectures for scalable image recognition"
    - "a survey on deep learning in medical image analysis"
    - "attentive recurrent comparators"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "deep residual learning for image recognition"
    - "particular object retrieval with integral max pooling of cnn activations"

Connected through hyperedge HE-6 (induced by source paper "transfer learning based detection of diabetic retinopathy from small dataset"):
  with members:
    - "deep learning on small datasets without pre training using cosine loss"
    - "deep learning vs human graders for classifying severity levels of diabetic retinopathy in a real world nationwide screening program"
    - "deep learning based computer aided diagnosis systems for diabetic retinopathy a survey"
    - "a survey on deep learning in medical image analysis"
    - "weakly supervised localization of diabetic retinopathy lesions in retinal fundus images"
    - "rethinking the inception architecture for computer vision"

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"
    - "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"
    - "segmentation of optic disc fovea and retinal vasculature using a single convolutional neural network"

Connected through hyperedge HE-8 (induced by source paper "detailed investigation of deep features with sparse representation and dimensionality reduction in cbir a comparative study"):
  with members:
    - "effects of distance measure choice on k nearest neighbor classifier performance a review"
    - "pilot comparative study of different deep features for palmprint identification in low quality images"
    - "selective deep convolutional features for image retrieval"
    - "unsupervised deep feature extraction for remote sensing image classification"
    - "very deep convolutional networks for large scale image recognition"
    - "image super resolution using deep convolutional networks"
    - "visual speech recognition"

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)