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: "disc aware ensemble network for glaucoma screening from fundus image"
  Abstract: Glaucoma is a chronic eye disease that leads to irreversible vision loss. Most of the existing automatic screening methods first segment the main structure and subsequently calculate the clinical measurement for the detection and screening of glaucoma. However, these measurement-based methods rely heavily on the segmentation accuracy and ignore various visual features. In this paper, we introduce a deep learning technique to gain additional image-relevant information and screen glaucoma from the fundus image directly. Specifically, a novel disc-aware ensemble network for automatic glaucoma screening is proposed, which integrates the deep hierarchical context of the global fundus image and the local optic disc region. Four deep streams on different levels and modules are, respectively, considered as global image stream, segmentation-guided network, local disc region stream, and disc polar transformation stream. Finally, the output probabilities of different streams are fused as the final screening result. The experiments on two glaucoma data sets (SCES and new SINDI data sets) show that our method outperforms other state-of-the-art algorithms.

Connected through hyperedge HE-1 (induced by source paper "ophthalmic diagnosis using deep learning with fundus images a critical review"):
  with members:
    - "pixelbnn augmenting the pixelcnn with batch normalization and the presentation of a fast architecture for retinal vessel segmentation"
    - "embedded deep learning in ophthalmology making ophthalmic imaging smarter"
    - "classification of findings with localized lesions in fundoscopic images using a regionally guided cnn"
    - "deep supervision with additional labels for retinal vessel segmentation task"
    - "deep visual domain adaptation a survey"
    - "predicting optical coherence tomography derived diabetic macular edema grades from fundus photographs using deep learning"
    - "deep learning based retinal oct segmentation"
    - "a deep learning based joint segmentation and classification framework for glaucoma assesment in retinal color fundus images"

Connected through hyperedge HE-2 (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"
    - "joint optic disc and cup segmentation based on multi label deep network and polar transformation"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "unsupervised cross modality domain adaptation of convnets for biomedical image segmentations with adversarial loss"

Connected through hyperedge HE-3 (induced by source paper "trk cnn transferable ranking cnn for image classification of glaucoma glaucoma suspect and normal eyes"):
  with members:
    - "2sranking cnn a 2 stage ranking cnn for diagnosis of glaucoma from fundus images using cam extracted roi as an intermediate input"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "learning deep features for discriminative localization"
    - "adam a method for stochastic optimization"
    - "going deeper with convolutions"

Connected through hyperedge HE-4 (induced by source paper "automated segmentation of the optic disk and cup using dual stage fully convolutional networks"):
  with members:
    - "joint optic disc and cup segmentation based on multi label deep network and polar transformation"
    - "refinenet multi path refinement networks for high resolution semantic segmentation"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"
    - "semantic image segmentation with deep convolutional nets and fully connected crfs"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-5 (induced by source paper "evaluation of retinal image quality assessment networks in different color spaces"):
  with members:
    - "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"
    - "structure preserving guided retinal image filtering and its application for optic disk analysis"
    - "systematic evaluation of convolution neural network advances on the imagenet"
    - "densely connected convolutional networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-6 (induced by source paper "evaluation of an ai system for the automated detection of glaucoma from stereoscopic optic disc photographs the european optic disc assessment study"):
  with members:
    - "deep residual learning for image recognition"

Connected through hyperedge HE-7 (induced by source paper "sdfn segmentation based deep fusion network for thoracic disease classification in chest x ray images"):
  with members:
    - "diagnose like a radiologist attention guided convolutional neural network for thorax disease classification"
    - "quicksilver fast predictive image registration a deep learning approach"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-8 (induced by source paper "multi context deep network for angle closure glaucoma screening in anterior segment oct"):
  with members:
    - "joint optic disc and cup segmentation based on multi label deep network and polar transformation"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "very deep convolutional networks for large scale image recognition"
    - "rich feature hierarchies for accurate object detection and 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)