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: "an ensemble based system for microaneurysm detection and diabetic retinopathy grading"
  Abstract: Reliable microaneurysm detection in digital fundus images is still an open issue in medical image processing. We propose an ensemble-based framework to improve microaneurysm detection. Unlike the well-known approach of considering the output of multiple classifiers, we propose a combination of internal components of microaneurysm detectors, namely preprocessing methods and candidate extractors. We have evaluated our approach for microaneurysm detection in an online competition, where this algorithm is currently ranked as first, and also on two other databases. Since microaneurysm detection is decisive in diabetic retinopathy (DR) grading, we also tested the proposed method for this task on the publicly available Messidor database, where a promising AUC 0.90 ± 0.01 is achieved in a “DR/non-DR”-type classification based on the presence or absence of the microaneurysms.

Connected through hyperedge HE-1 (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-2 (induced by source paper "deep learning based early detection and grading of diabetic retinopathy using retinal fundus images"):
  with members:
    - "ensemble of convolutional neural networks for automatic grading of diabetic retinopathy and macular edema"
    - "diabetic retinopathy detection via deep convolutional networks for discriminative localization and visual explanation"
    - "a deep learning interpretable classifier for diabetic retinopathy disease grading"
    - "lesion detection and grading of diabetic retinopathy via two stages deep convolutional neural networks"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "identity mappings in deep residual networks"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-3 (induced by source paper "retinal microaneurysms detection using local convergence index features"):
  with members:
    - "improved microaneurysm detection using deep neural networks"

Connected through hyperedge HE-4 (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"
    - "a gaussian scale space approach for exudates detection classification and severity prediction"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "an ensemble deep learning based approach for red lesion detection in fundus images"):
  with members:
    - "understanding the mechanisms of deep transfer learning for medical images"
    - "deep retinal image understanding"
    - "matconvnet convolutional neural networks for matlab"
    - "cnn features off the shelf an astounding baseline for recognition"
    - "visualizing and understanding convolutional networks"
    - "retinal vessel segmentation using the 2 d gabor wavelet and supervised classification"

Connected through hyperedge HE-6 (induced by source paper "classifier ensemble creation via false labelling"):
  with members:
    - "an ensemble based system for automatic screening of diabetic retinopathy"
    - "estimating continuous distributions in bayesian classifiers"

Connected through hyperedge HE-7 (induced by source paper "an ensemble based system for automatic screening of diabetic retinopathy"):
  with members:
    - "a two phase decision support framework for the automatic screening of digital fundus images"

Connected through hyperedge HE-8 (induced by source paper "an unsupervised ensemble based markov random field approach to microscope cell image segmentation"):
  with members:
    - "objective criteria for the evaluation of clustering methods"

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)