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: "patch based output space adversarial learning for joint optic disc and cup segmentation"
  Abstract: Glaucoma is a leading cause of irreversible blindness. Accurate segmentation of the optic disc (OD) and optic cup (OC) from fundus images is beneficial to glaucoma screening and diagnosis. Recently, convolutional neural networks demonstrate promising progress in the joint OD and OC segmentation. However, affected by the domain shift among different datasets, deep networks are severely hindered in generalizing across different scanners and institutions. In this paper, we present a novel patch-based output space adversarial learning framework (   ${p}$   OSAL) to jointly and robustly segment the OD and OC from different fundus image datasets. We first devise a lightweight and efficient segmentation network as a backbone. Considering the specific morphology of OD and OC, a novel morphology-aware segmentation loss is proposed to guide the network to generate accurate and smooth segmentation. Our    ${p}$   OSAL framework then exploits unsupervised domain adaptation to address the domain shift challenge by encouraging the segmentation in the target domain to be similar to the source ones. Since the whole-segmentation-based adversarial loss is insufficient to drive the network to capture segmentation details, we further design the    ${p}$   OSAL in a patch-based fashion to enable fine-grained discrimination on local segmentation details. We extensively evaluate our    ${p}$   OSAL framework and demonstrate its effectiveness in improving the segmentation performance on three public retinal fundus image datasets, i.e., Drishti-GS, RIM-ONE-r3, and REFUGE. Furthermore, our    ${p}$   OSAL framework achieved the first place in the OD and OC segmentation tasks in the  MICCAI 2018 Retinal Fundus Glaucoma Challenge .

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:
    - "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"
    - "joint optic disc and cup segmentation based on multi label deep network and polar transformation"
    - "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 "hierarchical attention networks for medical image segmentation"):
  with members:
    - "dunet a deformable network for retinal vessel segmentation"
    - "expectation maximization attention networks for semantic 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"
    - "recurrent residual convolutional neural network based on u net r2u net for medical image segmentation"

Connected through hyperedge HE-3 (induced by source paper "boundary and entropy driven adversarial learning for fundus image 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"
    - "semantic aware generative adversarial nets for unsupervised domain adaptation in chest x ray segmentation"
    - "joint optic disc and cup segmentation based on multi label deep network and polar transformation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "advent adversarial entropy minimization for domain adaptation in semantic segmentation"
    - "unsupervised cross modality domain adaptation of convnets for biomedical image segmentations with adversarial loss"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "et net a generic edge attention guidance network for medical image segmentation"):
  with members:
    - "salient object detection in the deep learning era an in depth survey"
    - "ce net context encoder network for 2d medical image segmentation"
    - "joint optic disc and cup segmentation based on multi label deep network and polar transformation"
    - "dcan deep contour aware networks for accurate gland segmentation"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "fully convolutional networks for semantic segmentation"

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)