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: "pathological oct retinal layer segmentation using branch residual u shape networks"
  Abstract: The automatic segmentation of retinal layer structures enables clinically-relevant quantification and monitoring of eye disorders over time in OCT imaging. Eyes with late-stage diseases are particularly challenging to segment, as their shape is highly warped due to pathological biomarkers. In this context, we propose a novel fully Convolutional Neural Network (CNN) architecture which combines dilated residual blocks in an asymmetric U-shape configuration, and can segment multiple layers of highly pathological eyes in one shot. We validate our approach on a dataset of late-stage AMD patients and demonstrate lower computational costs and higher performance compared to other state-of-the-art methods.

Connected through hyperedge HE-1 (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"
    - "joint optic disc and cup segmentation based on multi label deep network and polar transformation"
    - "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"
    - "improving generalization performance by switching from adam to sgd"

Connected through hyperedge HE-2 (induced by source paper "sparse gan sparsity constrained generative adversarial network for anomaly detection in retinal oct image"):
  with members:
    - "unsupervised identification of disease marker candidates in retinal oct imaging data"
    - "ce net context encoder network for 2d medical image segmentation"
    - "ganomaly semi supervised anomaly detection via adversarial training"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "image to image translation with conditional adversarial networks"
    - "learning deep features for discriminative localization"

Connected through hyperedge HE-3 (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"
    - "joint segmentation and uncertainty visualization of retinal layers in optical coherence tomography images using bayesian deep learning"
    - "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-4 (induced by source paper "fused detection of retinal biomarkers in oct volumes"):
  with members:
    - "dilated residual networks"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-5 (induced by source paper "learning to segment corneal tissue interfaces in oct images"):
  with members:
    - "joint segmentation and uncertainty visualization of retinal layers in optical coherence tomography images using bayesian deep learning"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "joint segmentation and uncertainty visualization of retinal layers in optical coherence tomography images using bayesian deep learning"):
  with members:
    - "relaynet retinal layer and fluid segmentation of macular optical coherence tomography using fully convolutional network"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "the one hundred layers tiramisu fully convolutional densenets for semantic segmentation"
    - "u net convolutional networks for biomedical image 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)