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: "joint optic disc and cup segmentation based on multi label deep network and polar transformation"
  Abstract: Glaucoma is a chronic eye disease that leads to irreversible vision loss. The cup to disc ratio (CDR) plays an important role in the screening and diagnosis of glaucoma. Thus, the accurate and automatic segmentation of optic disc (OD) and optic cup (OC) from fundus images is a fundamental task. Most existing methods segment them separately, and rely on hand-crafted visual feature from fundus images. In this paper, we propose a deep learning architecture, named M-Net, which solves the OD and OC segmentation jointly in a one-stage multi-label system. The proposed M-Net mainly consists of multi-scale input layer, U-shape convolutional network, side-output layer, and multi-label loss function. The multi-scale input layer constructs an image pyramid to achieve multiple level receptive field sizes. The U-shape convolutional network is employed as the main body network structure to learn the rich hierarchical representation, while the side-output layer acts as an early classifier that produces a companion local prediction map for different scale layers. Finally, a multi-label loss function is proposed to generate the final segmentation map. For improving the segmentation performance further, we also introduce the polar transformation, which provides the representation of the original image in the polar coordinate system. The experiments show that our M-Net system achieves state-of-the-art OD and OC segmentation result on ORIGA data set. Simultaneously, the proposed method also obtains the satisfactory glaucoma screening performances with calculated CDR value on both ORIGA and SCES datasets.

Connected through hyperedge HE-1 (induced by source paper "refuge challenge a unified framework for evaluating automated methods for glaucoma assessment from fundus photographs"):
  with members:
    - "patch based output space adversarial learning for joint optic disc and cup segmentation"
    - "classification of findings with localized lesions in fundoscopic images using a regionally guided cnn"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "predicting cardiovascular risk factors in retinal fundus photographs using deep learning"
    - "why rankings of biomedical image analysis competitions should be interpreted with care"
    - "deep learning to assess glaucoma risk and associated features in fundus images"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "stack u net refinement network for image segmentation on the example of optic disc and cup"

Connected through hyperedge HE-2 (induced by source paper "multi organ segmentation over partially labeled datasets with multi scale feature abstraction"):
  with members:
    - "boundary weighted domain adaptive neural network for prostate mr image segmentation"
    - "learning a deep convnet for multi label classification with partial labels"
    - "use net incorporating squeeze and excitation blocks into u net for prostate zonal segmentation of multi institutional mri datasets"
    - "feature fusion encoder decoder network for automatic liver lesion segmentation"
    - "ce net context encoder network for 2d medical image segmentation"
    - "the liver tumor segmentation benchmark lits"
    - "abdominal multi organ segmentation with organ attention networks and statistical fusion"
    - "training of convolutional networks on multiple heterogeneous datasets for street scene semantic segmentation"

Connected through hyperedge HE-3 (induced by source paper "hierarchical attention networks for medical image segmentation"):
  with members:
    - "dunet a deformable network for retinal vessel segmentation"
    - "expectation maximization attention networks for semantic segmentation"
    - "patch based output space adversarial learning for joint optic disc and cup segmentation"
    - "mixhop higher order graph convolutional architectures via sparsified neighborhood mixing"
    - "a simple pooling based design for real time salient object detection"
    - "ce net context encoder network for 2d medical image segmentation"
    - "et net a generic edge attention guidance network for medical image segmentation"
    - "fast interactive object annotation with curve gcn"

Connected through hyperedge HE-4 (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"
    - "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"
    - "attention branch network learning of attention mechanism for visual explanation"

Connected through hyperedge HE-5 (induced by source paper "ce net context encoder network for 2d medical image segmentation"):
  with members:
    - "rethinking imagenet pre training"
    - "fully convolutional adaptation networks for semantic segmentation"
    - "infinet fully convolutional networks for infant brain mri segmentation"
    - "sparse range constrained learning and its application for medical image grading"
    - "large kernel matters improve semantic segmentation by global convolutional network"
    - "optic disc and cup segmentation methods for glaucoma detection with modification of u net convolutional neural network"
    - "pathological oct retinal layer segmentation using branch residual u shape networks"
    - "improving generalization performance by switching from adam to sgd"

Connected through hyperedge HE-6 (induced by source paper "patch based output space adversarial learning for joint optic disc and cup segmentation"):
  with members:
    - "task driven generative modeling for unsupervised domain adaptation application to x ray image segmentation"
    - "learning to adapt structured output space for semantic segmentation"
    - "translating and segmenting multimodal medical volumes with cycle and shape consistency generative adversarial network"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "semantic aware generative adversarial nets for unsupervised domain adaptation in chest x ray segmentation"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "disc aware ensemble network for glaucoma screening from fundus image"
    - "unsupervised cross modality domain adaptation of convnets for biomedical image segmentations with adversarial loss"

Connected through hyperedge HE-7 (induced by source paper "ddnet cartesian polar dual domain network for the joint optic disc and cup segmentation"):
  with members:
    - "template matching via densities on the roto translation group"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "cbam convolutional block attention module"
    - "polar transformer networks"
    - "optic disc and cup segmentation methods for glaucoma detection with modification of u net convolutional neural network"
    - "deep retinal image understanding"
    - "group equivariant convolutional networks"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-8 (induced by source paper "from patch to image segmentation using fully convolutional networks application to retinal images"):
  with members:
    - "convolutional neural networks for medical image analysis full training or fine tuning"
    - "a survey on deep learning in medical image analysis"
    - "segmentation of optic disc fovea and retinal vasculature using a single convolutional neural network"
    - "a fully convolutional neural network based structured prediction approach towards the retinal vessel segmentation"
    - "densely connected convolutional networks"
    - "holistically nested edge detection"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"

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)