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: "weakly supervised localization of diabetic retinopathy lesions in retinal fundus images"
  Abstract: Convolutional neural networks (CNNs) show impressive performance for image classification and detection, extending heavily to the medical image domain. Nevertheless, medical experts are sceptical in these predictions as the nonlinear multilayer structure resulting in a classification outcome is not directly graspable. Recently, approaches have been shown which help the user to understand the discriminative regions within an image which are decisive for the CNN to conclude to a certain class. Although these approaches could help to build trust in the CNNs predictions, they are only slightly shown to work with medical image data which often poses a challenge as the decision for a class relies on different lesion areas scattered around the entire image. Using the DiaretDB1 dataset, we show that on retina images different lesion areas fundamental for diabetic retinopathy are detected on an image level with high accuracy, comparable or exceeding supervised methods. On lesion level, we achieve few false positives with high sensitivity, though, the network is solely trained on image-level labels which do not include information about existing lesions. Classifying between diseased and healthy images, we achieve an AUC of 0.954 on the DiaretDB1.

Connected through hyperedge HE-1 (induced by source paper "a survey on deep learning of small sample in biomedical image analysis"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "synergistic image and feature adaptation towards cross modality domain adaptation for medical image segmentation"
    - "generative adversarial network in medical imaging a review"
    - "nnu net breaking the spell on successful medical image segmentation"
    - "constrained cnn losses for weakly supervised segmentation"
    - "angle closure detection in anterior segment oct based on multilevel deep network"
    - "chestnet a deep neural network for classification of thoracic diseases on chest radiography"
    - "boxnet deep learning based biomedical image segmentation using boxes only annotation"

Connected through hyperedge HE-2 (induced by source paper "software and application patterns for explanation methods"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "unmasking clever hans predictors and assessing what machines really learn"
    - "evaluating neural network explanation methods using hybrid documents and morphological agreement"
    - "backprop evolution"
    - "innvestigate neural networks"
    - "methods for interpreting and understanding deep neural networks"
    - "towards computational fluorescence microscopy machine learning based integrated prediction of morphological and molecular tumor profiles"
    - "smoothgrad removing noise by adding noise"

Connected through hyperedge HE-3 (induced by source paper "interpretable and fine grained visual explanations for convolutional neural networks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "towards explanation of dnn based prediction with guided feature inversion"
    - "visual interpretability for deep learning a survey"
    - "regional multi scale approach for visually pleasing explanations of deep neural networks"
    - "techniques for interpretable machine learning"
    - "local explanation methods for deep neural networks lack sensitivity to parameter values"
    - "a theoretical explanation for perplexing behaviors of backpropagation based visualizations"
    - "explaining image classifiers by counterfactual generation"

Connected through hyperedge HE-4 (induced by source paper "transformation consistent self ensembling model for semi supervised medical image segmentation"):
  with members:
    - "the liver tumor segmentation benchmark lits"
    - "not so supervised a survey of semi supervised multi instance and transfer learning in medical image analysis"
    - "unsupervised domain adaptation for automatic estimation of cardiothoracic ratio"
    - "adversarial learning for semi supervised semantic segmentation"
    - "unsupervised reverse domain adaptation for synthetic medical images via adversarial training"
    - "discriminative localization in cnns for weakly supervised segmentation of pulmonary nodules"
    - "isic 2017 skin lesion analysis towards melanoma detection"
    - "segmentation of optic disc fovea and retinal vasculature using a single convolutional neural network"

Connected through hyperedge HE-5 (induced by source paper "deep weakly supervised learning methods for classification and localization in histology images a survey"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "weakly supervised object localization using min max entropy an interpretable framework"
    - "high resolution representations for labeling pixels and regions"
    - "attention based deep multiple instance learning"
    - "deep self taught learning for weakly supervised object localization"
    - "explainable artificial intelligence understanding visualizing and interpreting deep learning models"
    - "constrained deep weak supervision for histopathology image segmentation"
    - "3d fully convolutional networks for subcortical segmentation in mri a large scale study"

Connected through hyperedge HE-6 (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"
    - "deep image mining for diabetic retinopathy screening"
    - "explaining nonlinear classification decisions with deep taylor decomposition"
    - "axiomatic attribution for deep networks"

Connected through hyperedge HE-7 (induced by source paper "attention based glaucoma detection a large scale database and cnn model"):
  with members:
    - "predicting cardiovascular risk factors in retinal fundus photographs using deep learning"
    - "chexnet radiologist level pneumonia detection on chest x rays with deep learning"
    - "thoracic disease identification and localization with limited supervision"
    - "discriminative localization in cnns for weakly supervised segmentation of pulmonary nodules"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "bubbleview an interface for crowdsourcing image importance maps and tracking visual attention"
    - "supervising neural attention models for video captioning by human gaze data"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-8 (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"
    - "deep image mining for diabetic retinopathy screening"
    - "a survey on deep learning in medical image analysis"
    - "rethinking the inception architecture for computer vision"

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)