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: "a gaussian scale space approach for exudates detection classification and severity prediction"
  Abstract: In the context of Computer Aided Diagnosis system for diabetic retinopathy, we present a novel method for detection of exudates and their classification for disease severity prediction. The method is based on Gaussian scale space based interest map and mathematical morphology. It makes use of support vector machine for classification and location information of the optic disc and the macula region for severity prediction. It can efficiently handle luminance variation and it is suitable for varied sized exudates. The method has been probed in publicly available DIARETDB1V2 and e-ophthaEX databases. For exudate detection the proposed method achieved a sensitivity of 96.54% and prediction of 98.35% in DIARETDB1V2 database.

Connected through hyperedge HE-1 (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-2 (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"
    - "improved microaneurysm detection using deep neural networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "traffic sign classification using deep inception based convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "lesion detection and grading of diabetic retinopathy via two stages deep convolutional neural networks"):
  with members:
    - "recent advances in convolutional neural networks"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "rethinking the inception architecture for computer vision"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-4 (induced by source paper "weakly supervised localization of diabetic retinopathy lesions in retinal fundus images"):
  with members:
    - "deep image mining for diabetic retinopathy screening"
    - "improved microaneurysm detection using deep neural networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "learning deep features for discriminative localization"
    - "object detectors emerge in deep scene cnns"
    - "self taught object localization with deep networks"
    - "visualizing and understanding convolutional networks"
    - "deep inside convolutional networks visualising image classification models and saliency maps"

Connected through hyperedge HE-5 (induced by source paper "improved microaneurysm detection using deep neural networks"):
  with members:
    - "an ensemble based system for automatic screening of diabetic retinopathy"

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)