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: "ensemble of convolutional neural networks for automatic grading of diabetic retinopathy and macular edema"
  Abstract: In this manuscript, we automate the procedure of grading of diabetic retinopathy and macular edema from fundus images using an ensemble of convolutional neural networks. The availability of limited amount of labeled data to perform supervised learning was circumvented by using transfer learning approach. The models in the ensemble were pre-trained on a large dataset comprising natural images and were later fine-tuned with the limited data for the task of choice. For an image, the ensemble of classifiers generate multiple predictions, and a max-voting based approach was utilized to attain the final grade of the anomaly in the image. For the task of grading DR, on the test data (n=56), the ensemble achieved an accuracy of 83.9\%, while for the task for grading macular edema the network achieved an accuracy of 95.45% (n=44).

Connected through hyperedge HE-1 (induced by source paper "advances in computer aided diagnosis of diabetic retinopathy"):
  with members:
    - "diabetic retinopathy detection via deep convolutional networks for discriminative localization and visual explanation"
    - "lesion detection and grading of diabetic retinopathy via two stages deep convolutional neural networks"

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:
    - "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"
    - "an ensemble based system for microaneurysm detection and diabetic retinopathy grading"

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)