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: "cycle consistent adversarial denoising network for multiphase coronary ct angiography"
  Abstract: In coronary CT angiography, a series of CT images are taken at different levels of radiation dose during the examination. Although this reduces the total radiation dose, the image quality during the low-dose phases is significantly degraded. To address this problem, here we propose a novel semi-supervised learning technique that can remove the noises of the CT images obtained in the low-dose phases by learning from the CT images in the routine dose phases. Although a supervised learning approach is not possible due to the differences in the underlying heart structure in two phases, the images in the two phases are closely related so that we propose a cycle-consistent adversarial denoising network to learn the non-degenerate mapping between the low and high dose cardiac phases. Experimental results showed that the proposed method effectively reduces the noise in the low-dose CT image while the preserving detailed texture and edge information. Moreover, thanks to the cyclic consistency and identity loss, the proposed network does not create any artificial features that are not present in the input images. Visual grading and quality evaluation also confirm that the proposed method provides significant improvement in diagnostic quality.

Connected through hyperedge HE-1 (induced by source paper "unsupervised medical image translation using cycle medgan"):
  with members:
    - "medgan medical image translation using gans"
    - "adversarial inpainting of medical image modalities"
    - "generative adversarial network in medical imaging a review"
    - "retrospective correction of rigid and non rigid mr motion artifacts using gans"
    - "compressed sensing mri reconstruction using a generative adversarial network with a cyclic loss"
    - "stacked u nets a no frills approach to natural image segmentation"
    - "generative adversarial networks for image to image translation on multi contrast mr images a comparison of cyclegan and unit"
    - "the unreasonable effectiveness of deep features as a perceptual metric"

Connected through hyperedge HE-2 (induced by source paper "optimal transport cyclegan and penalized ls for unsupervised learning in inverse problems"):
  with members:
    - "modl model based deep learning architecture for inverse problems"
    - "deep compressed sensing"
    - "k space deep learning for accelerated mri"
    - "fastmri an open dataset and benchmarks for accelerated mri"
    - "a deep convolutional neural network using directional wavelets for low dose x ray ct reconstruction"
    - "learning deep cnn denoiser prior for image restoration"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-3 (induced by source paper "three dimensional blind image deconvolution for fluorescence microscopy using generative adversarial networks"):
  with members:
    - "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"
    - "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-4 (induced by source paper "blind deconvolution microscopy using cycle consistent cnn with explicit psf layer"):
  with members:
    - "deblurgan blind motion deblurring using conditional adversarial networks"
    - "deep learning microscopy"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "attribute guided face generation using conditional cyclegan"
    - "instance normalization the missing ingredient for fast stylization"
    - "least squares generative adversarial networks"
    - "image to image translation with conditional adversarial 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)