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: "deep retinal image understanding"
  Abstract: This paper presents Deep Retinal Image Understanding (DRIU), a unified framework of retinal image analysis that provides both retinal vessel and optic disc segmentation. We make use of deep Convolutional Neural Networks (CNNs), which have proven revolutionary in other fields of computer vision such as object detection and image classification, and we bring their power to the study of eye fundus images. DRIU uses a base network architecture on which two set of specialized layers are trained to solve both the retinal vessel and optic disc segmentation. We present experimental validation, both qualitative and quantitative, in four public datasets for these tasks. In all of them, DRIU presents super-human performance, that is, it shows results more consistent with a gold standard than a second human annotator used as control.

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"
    - "disc aware ensemble network for glaucoma screening from fundus image"

Connected through hyperedge HE-2 (induced by source paper "on the evaluation and real world usage scenarios of deep vessel segmentation for retinography"):
  with members:
    - "dunet a deformable network for retinal vessel segmentation"
    - "retinamask learning to predict masks improves state of the art single shot detection for free"
    - "adaptive gradient methods with dynamic bound of learning rate"
    - "data efficient image recognition with contrastive predictive coding"
    - "fcos fully convolutional one stage object detection"
    - "mixmatch a holistic approach to semi supervised learning"
    - "ternausnetv2 fully convolutional network for instance segmentation"
    - "holistically nested edge detection"

Connected through hyperedge HE-3 (induced by source paper "a two stream meticulous processing network for retinal vessel segmentation"):
  with members:
    - "ce net context encoder network for 2d medical image segmentation"
    - "deep supervision with additional labels for retinal vessel segmentation task"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-4 (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-5 (induced by source paper "recurrent u net for resource constrained segmentation"):
  with members:
    - "fast scnn fast semantic segmentation network"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "group normalization"
    - "analysis of hand segmentation in the wild"
    - "contextnet exploring context and detail for semantic segmentation in real time"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "icnet for real time semantic segmentation on high resolution images"
    - "beyond the pixel wise loss for topology aware delineation"

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"
    - "joint optic disc and cup segmentation based on multi label deep network and polar transformation"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "disc aware ensemble network for glaucoma screening from fundus image"

Connected through hyperedge HE-7 (induced by source paper "learning likelihoods with conditional normalizing flows"):
  with members:
    - "integer discrete flows and lossless compression"
    - "emerging convolutions for generative normalizing flows"
    - "guided image generation with conditional invertible neural networks"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "unsupervised image super resolution using cycle in cycle generative adversarial networks"
    - "ffjord free form continuous dynamics for scalable reversible generative models"
    - "learning to maintain natural image statistics"
    - "pixelcnn improving the pixelcnn with discretized logistic mixture likelihood and other modifications"

Connected through hyperedge HE-8 (induced by source paper "metric based regularization and temporal ensemble for multi task learning using heterogeneous unsupervised tasks"):
  with members:
    - "transfer adaptation learning a decade survey"
    - "boosting self supervised learning via knowledge transfer"
    - "improvements to context based self supervised learning"
    - "multi task self supervised visual learning"
    - "a survey on deep learning in medical image analysis"
    - "w net a deep model for fully unsupervised image segmentation"
    - "a survey on multi task learning"
    - "generic 3d representation via pose estimation and matching"

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)