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: "lesion detection and grading of diabetic retinopathy via two stages deep convolutional neural networks"
  Abstract: We propose an automatic diabetic retinopathy (DR) analysis algorithm based on two-stages deep convolutional neural networks (DCNN). Compared to existing DCNN-based DR detection methods, the proposed algorithm have the following advantages: (1) Our method can point out the location and type of lesions in the fundus images, as well as giving the severity grades of DR. Moreover, since retina lesions and DR severity appear with different scales in fundus images, the integration of both local and global networks learn more complete and specific features for DR analysis. (2) By introducing imbalanced weighting map, more attentions will be given to lesion patches for DR grading, which significantly improve the performance of the proposed algorithm. In this study, we label 12,206 lesion patches and re-annotate the DR grades of 23,595 fundus images from Kaggle competition dataset. Under the guidance of clinical ophthalmologists, the experimental results show that our local lesion detection net achieve comparable performance with trained human observers, and the proposed imbalanced weighted scheme also be proved to significantly improve the capability of our DCNN-based DR grading algorithm.

Connected through hyperedge HE-1 (induced by source paper "learn to segment retinal lesions and beyond"):
  with members:
    - "ce net context encoder network for 2d medical image segmentation"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "weakly supervised instance segmentation using class peak response"
    - "learning a discriminative feature network for semantic segmentation"
    - "joint optic disc and cup segmentation based on multi label deep network and polar transformation"
    - "ccnet criss cross attention for semantic segmentation"
    - "multi evidence filtering and fusion for multi label classification object detection and semantic segmentation based on weakly supervised learning"
    - "weakly supervised semantic segmentation by iteratively mining common object features"

Connected through hyperedge HE-2 (induced by source paper "mini lesions detection on diabetic retinopathy images via large scale cnn features"):
  with members:
    - "ssd single shot multibox detector"
    - "feature pyramid networks for object detection"
    - "you only look once unified real time object detection"
    - "deep residual learning for image recognition"
    - "fast r cnn"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-3 (induced by source paper "learning mutually local global u nets for high resolution retinal lesion segmentation in fundus images"):
  with members:
    - "v net fully convolutional neural networks for volumetric medical image segmentation"
    - "holistically nested edge detection"
    - "u net convolutional networks for biomedical image segmentation"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "advances in computer aided diagnosis of diabetic retinopathy"):
  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"

Connected through hyperedge HE-5 (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"
    - "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)