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: "relaynet retinal layer and fluid segmentation of macular optical coherence tomography using fully convolutional network"
  Abstract: Optical coherence tomography (OCT) is used for non-invasive diagnosis of diabetic macular edema assessing the retinal layers. In this paper, we propose a new fully convolutional deep architecture, termed ReLayNet, for end-to-end segmentation of retinal layers and fluid masses in eye OCT scans. ReLayNet uses a contracting path of convolutional blocks (encoders) to learn a hierarchy of contextual features, followed by an expansive path of convolutional blocks (decoders) for semantic segmentation. ReLayNet is trained to optimize a joint loss function comprising of weighted logistic regression and Dice overlap loss. The framework is validated on a publicly available benchmark dataset with comparisons against five state-of-the-art segmentation methods including two deep learning based approaches to substantiate its effectiveness.

Connected through hyperedge HE-1 (induced by source paper "a deep learning approach to denoise optical coherence tomography images of the optic nerve head"):
  with members:
    - "classification of mri data using deep learning and gaussian process based model selection"
    - "retinal fluid segmentation and detection in optical coherence tomography images using fully convolutional neural network"
    - "denoising of 3d magnetic resonance images with multi channel residual learning of convolutional neural network"
    - "richer convolutional features for edge detection"
    - "imagej2 imagej for the next generation of scientific image data"
    - "medical image denoising using convolutional denoising autoencoders"
    - "multiframe denoising of high speed optical coherence tomography data using interframe and intraframe priors"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-2 (induced by source paper "joint segmentation and uncertainty visualization of retinal layers in optical coherence tomography images using bayesian deep learning"):
  with members:
    - "pathological oct retinal layer segmentation using branch residual u shape networks"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "segmentation of retinal cysts from optical coherence tomography volumes via selective enhancement"):
  with members:
    - "domain knowledge assisted cyst segmentation in oct retinal images"
    - "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)