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 segmentation and uncertainty visualization of retinal layers in optical coherence tomography images using bayesian deep learning"
  Abstract: Optical coherence tomography (OCT) is commonly used to analyze retinal layers for assessment of ocular diseases. In this paper, we propose a method for retinal layer segmentation and quantification of uncertainty based on Bayesian deep learning. Our method not only performs end-to-end segmentation of retinal layers, but also gives the pixel wise uncertainty measure of the segmentation output. The generated uncertainty map can be used to identify erroneously segmented image regions which is useful in downstream analysis. We have validated our method on a dataset of 1487 images obtained from 15 subjects (OCT volumes) and compared it against the state-of-the-art segmentation algorithms that does not take uncertainty into account. The proposed uncertainty based segmentation method results in comparable or improved performance, and most importantly is more robust against noise.

Connected through hyperedge HE-1 (induced by source paper "uncertainty guided domain alignment for layer segmentation in oct images"):
  with members:
    - "learning to adapt structured output space for semantic segmentation"
    - "unsupervised domain adaptation for automatic estimation of cardiothoracic ratio"
    - "exploring uncertainty measures in deep networks for multiple sclerosis lesion detection and segmentation"
    - "a probabilistic u net for segmentation of ambiguous images"
    - "unsupervised cross modality domain adaptation of convnets for biomedical image segmentations with adversarial loss"
    - "adversarial domain adaptation for classification of prostate histopathology whole slide images"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "curriculum domain adaptation for semantic segmentation of urban scenes"

Connected through hyperedge HE-2 (induced by source paper "u2 net a bayesian u net model with epistemic uncertainty feedback for photoreceptor layer segmentation in pathological oct scans"):
  with members:
    - "exploring uncertainty measures in deep networks for multiple sclerosis lesion detection and segmentation"
    - "pathological oct retinal layer segmentation using branch residual u shape networks"
    - "bayesian segnet model uncertainty in deep convolutional encoder decoder architectures for scene understanding"
    - "u net convolutional networks for biomedical image segmentation"
    - "empirical evaluation of rectified activations in convolutional network"

Connected through hyperedge HE-3 (induced by source paper "efficient active learning for image classification and segmentation using a sample selection and conditional generative adversarial network"):
  with members:
    - "night time haze and glow removal using deep dilated convolutional network"
    - "informative sample generation using class aware generative adversarial networks for classification of chest xrays"
    - "progressive generative adversarial networks for medical image super resolution"
    - "combining transfer learning and segmentation information with gans for training data independent image registration"
    - "amd severity prediction and explainability using image registration and deep embedded clustering"
    - "training data independent image registration with gans using transfer learning and segmentation information"
    - "szoom a framework for automatic zoom into high resolution surveillance videos"
    - "deep multiscale convolutional feature learning for weakly supervised localization of chest pathologies in x ray images"

Connected through hyperedge HE-4 (induced by source paper "gan based medical image registration"):
  with members:
    - "informative sample generation using class aware generative adversarial networks for classification of chest xrays"
    - "training data independent image registration with gans using transfer learning and segmentation information"
    - "deep multiscale convolutional feature learning for weakly supervised localization of chest pathologies in x ray images"
    - "efficient active learning for image classification and segmentation using a sample selection and conditional generative adversarial network"
    - "end to end unsupervised deformable image registration with a convolutional neural network"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "an artificial agent for robust image registration"
    - "photo realistic single image super resolution using a generative adversarial network"

Connected through hyperedge HE-5 (induced by source paper "learning to segment corneal tissue interfaces in oct images"):
  with members:
    - "pathological oct retinal layer segmentation using branch residual u shape networks"
    - "the one hundred layers tiramisu fully convolutional densenets for semantic segmentation"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "multi scale context aggregation by dilated convolutions"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "learning deconvolution network for semantic segmentation"
    - "adam a method for stochastic optimization"

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)