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: "ct image denoising with perceptive deep neural networks"
  Abstract: Increasing use of CT in modern medical practice has raised concerns over associated radiation dose. Reduction of radiation dose associated with CT can increase noise and artifacts, which can adversely affect diagnostic confidence. Denoising of low-dose CT images on the other hand can help improve diagnostic confidence, which however is a challenging problem due to its ill-posed nature, since one noisy image patch may correspond to many different output patches. In the past decade, machine learning based approaches have made quite impressive progress in this direction. However, most of those methods, including the recently popularized deep learning techniques, aim for minimizing mean-squared-error (MSE) between a denoised CT image and the ground truth, which results in losing important structural details due to over-smoothing, although the PSNR based performance measure looks great. In this work, we introduce a new perceptual similarity measure as the objective function for a deep convolutional neural network to facilitate CT image denoising. Instead of directly computing MSE for pixel-to-pixel intensity loss, we compare the perceptual features of a denoised output against those of the ground truth in a feature space. Therefore, our proposed method is capable of not only reducing the image noise levels, but also keeping the critical structural information at the same time. Promising results have been obtained in our experiments with a large number of CT images.

Connected through hyperedge HE-1 (induced by source paper "sharpness aware low dose ct denoising using conditional generative adversarial network"):
  with members:
    - "low dose ct image denoising using a generative adversarial network with wasserstein distance and perceptual loss"
    - "a deep convolutional neural network using directional wavelets for low dose x ray ct reconstruction"
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"
    - "spatially varying blur detection based on multiscale fused and sorted transform coefficients of gradient magnitudes"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved training of wasserstein gans"
    - "image de raining using a conditional generative adversarial network"
    - "the pose knows video forecasting by generating pose futures"

Connected through hyperedge HE-2 (induced by source paper "unsupervised semi supervised deep learning for low dose ct enhancement"):
  with members:
    - "sharpness aware low dose ct denoising using conditional generative adversarial network"
    - "a deep convolutional neural network using directional wavelets for low dose x ray ct reconstruction"
    - "deep predictive coding networks for video prediction and unsupervised learning"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "framing u net via deep convolutional framelets application to sparse view ct"):
  with members:
    - "low dose ct image denoising using a generative adversarial network with wasserstein distance and perceptual loss"
    - "a deep convolutional neural network using directional wavelets for low dose x ray ct reconstruction"
    - "wavelet domain residual network wavresnet for low dose x ray ct reconstruction"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "deep convolutional neural network for inverse problems in imaging"
    - "real time single image and video super resolution using an efficient sub pixel convolutional neural network"
    - "image restoration using very deep convolutional encoder decoder networks with symmetric skip connections"
    - "deep residual learning for compressed sensing ct reconstruction via persistent homology analysis"

Connected through hyperedge HE-4 (induced by source paper "towards ct quality ultrasound imaging using deep learning"):
  with members:
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "deep convolutional neural network for inverse problems in imaging"
    - "image restoration using convolutional auto encoders with symmetric skip connections"

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)