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: "tubule segmentation of fluorescence microscopy images based on convolutional neural networks with inhomogeneity correction"
  Abstract: Fluorescence microscopy has become a widely used tool for studying various biological structures of in vivo tissue or cells. However, quantitative analysis of these biological structures remains a challenge due to their complexity which is exacerbated by distortions caused by lens aberrations and light scattering. Moreover, manual quantification of such image volumes is an intractable and error-prone process, making the need for automated image analysis methods crucial. This paper describes a segmentation method for tubular structures in fluorescence microscopy images using convolutional neural networks with data augmentation and inhomogeneity correction. The segmentation results of the proposed method are visually and numerically compared with other microscopy segmentation methods. Experimental results indicate that the proposed method has better performance with correctly segmenting and identifying multiple tubular structures compared to other methods.

Connected through hyperedge HE-1 (induced by source paper "three dimensional blind image deconvolution for fluorescence microscopy using generative adversarial networks"):
  with members:
    - "cycle consistent adversarial denoising network for multiphase coronary ct angiography"
    - "generative adversarial network in medical imaging a review"
    - "three dimensional fluorescence microscopy image synthesis and segmentation"
    - "semi blind spatially variant deconvolution in optical microscopy with local point spread function estimation by use of convolutional neural networks"
    - "background subtraction using the factored 3 way restricted boltzmann machines"
    - "isotropic reconstruction of 3d fluorescence microscopy images using convolutional neural networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "a survey on deep learning in medical image analysis"

Connected through hyperedge HE-2 (induced by source paper "advanced deep convolutional neural network approaches for digital pathology image analysis a comprehensive evaluation with different use cases"):
  with members:
    - "epithelium segmentation using deep learning in h e stained prostate specimens with immunohistochemistry as reference standard"
    - "microscopic nuclei classification segmentation and detection with improved deep convolutional neural network dcnn approaches"
    - "recurrent residual convolutional neural network based on u net r2u net for medical image segmentation"
    - "automated diagnosis of lymphoma with digital pathology images using deep learning"
    - "a survey on deep learning in medical image analysis"
    - "improved inception residual convolutional neural network for object recognition"
    - "inception recurrent convolutional neural network for object recognition"
    - "automatic lymphocyte detection in h e images with deep neural networks"

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)