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: "isotropic reconstruction of 3d fluorescence microscopy images using convolutional neural networks"
  Abstract: Fluorescence microscopy images usually show severe anisotropy in axial versus lateral resolution. This hampers downstream processing, i.e. the automatic extraction of quantitative biological data. While deconvolution methods and other techniques to address this problem exist, they are either time consuming to apply or limited in their ability to remove anisotropy. We propose a method to recover isotropic resolution from readily acquired anisotropic data. We achieve this using a convolutional neural network that is trained end-to-end from the same anisotropic body of data we later apply the network to. The network effectively learns to restore the full isotropic resolution by restoring the image under a trained, sample specific image prior. We apply our method to $3$ synthetic and $3$ real datasets and show that our results improve on results from deconvolution and state-of-the-art super-resolution techniques. Finally, we demonstrate that a standard 3D segmentation pipeline performs on the output of our network with comparable accuracy as on the full isotropic data.

Connected through hyperedge HE-1 (induced by source paper "first image then video a two stage network for spatiotemporal video denoising"):
  with members:
    - "deep video inpainting"
    - "noisy as clean learning unsupervised denoising from the corrupted image"
    - "noise2self blind denoising by self supervision"
    - "videnn deep blind video denoising"
    - "fastdvdnet towards real time video denoising without explicit motion estimation"
    - "noise2void learning denoising from single noisy images"
    - "noise2noise learning image restoration without clean data"
    - "non local video denoising by cnn"

Connected through hyperedge HE-2 (induced by source paper "three dimensional virtual refocusing of fluorescence microscopy images using deep learning"):
  with members:
    - "bright field holography cross modality deep learning enables snapshot 3d imaging with bright field contrast using a single hologram"
    - "resolution enhancement in scanning electron microscopy using deep learning"
    - "extended depth of field in holographic imaging using deep learning based autofocusing and phase recovery"
    - "deep learning enhanced mobile phone microscopy"
    - "deep learning based super resolution in coherent imaging systems"
    - "deep learning microscopy"
    - "diffusercam lensless single exposure 3d imaging"
    - "3d r2n2 a unified approach for single and multi view 3d object reconstruction"

Connected through hyperedge HE-3 (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"
    - "tubule segmentation of fluorescence microscopy images based on convolutional neural networks with inhomogeneity correction"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "a survey on deep learning in medical image analysis"

Connected through hyperedge HE-4 (induced by source paper "noise2void learning denoising from single noisy images"):
  with members:
    - "noise2self blind denoising by self supervision"
    - "cryo care content aware image restoration for cryo transmission electron microscopy data"
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "toward convolutional blind denoising of real photographs"
    - "noise2noise learning image restoration without clean data"
    - "deep image prior"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "memnet a persistent memory network for image restoration"

Connected through hyperedge HE-5 (induced by source paper "cell segmentation in 3d confocal images using supervoxel merge forests with cnn based hypothesis selection"):
  with members:
    - "image segmentation using hierarchical merge tree"

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)