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: "sharpness aware low dose ct denoising using conditional generative adversarial network"
  Abstract: Low-dose computed tomography (LDCT) has offered tremendous benefits in radiation-restricted applications, but the quantum noise as resulted by the insufficient number of photons could potentially harm the diagnostic performance. Current image-based denoising methods tend to produce a blur effect on the final reconstructed results especially in high noise levels. In this paper, a deep learning-based approach was proposed to mitigate this problem. An adversarially trained network and a sharpness detection network were trained to guide the training process. Experiments on both simulated and real dataset show that the results of the proposed method have very small resolution loss and achieves better performance relative to state-of-the-art methods both quantitatively and visually.

Connected through hyperedge HE-1 (induced by source paper "automatic catheter detection in pediatric x ray images using a scale recurrent network and synthetic data"):
  with members:
    - "scale recurrent network for deep image deblurring"
    - "fully automatic and real time catheter segmentation in x ray fluoroscopy"
    - "scan structure correcting adversarial network for organ segmentation in chest x rays"
    - "deep residual learning for image recognition"
    - "convolutional lstm network a machine learning approach for precipitation nowcasting"
    - "fully convolutional networks for semantic segmentation"
    - "retinal vessel segmentation using the 2 d gabor wavelet and supervised classification"

Connected through hyperedge HE-2 (induced by source paper "glyphgan style consistent font generation based on generative adversarial networks"):
  with members:
    - "automatic generation of typographic font from small font subset"
    - "semi supervised fusedgan for conditional image generation"
    - "word level font to font image translation using convolutional recurrent generative adversarial networks"
    - "chinese typography transfer"
    - "auto encoder guided gan for chinese calligraphy synthesis"
    - "stackgan realistic image synthesis with stacked generative adversarial networks"
    - "improved training of wasserstein gans"
    - "aligngan learning to align cross domain images with conditional generative adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "few shot meta denoising"):
  with members:
    - "adversarial signal denoising with encoder decoder networks"
    - "unprocessing images for learned raw denoising"
    - "prototypical networks for few shot learning"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "a wavenet for speech denoising"
    - "xunit learning a spatial activation function for efficient image restoration"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "learning to learn by gradient descent by gradient descent"

Connected through hyperedge HE-4 (induced by source paper "cycle consistent adversarial denoising network for multiphase coronary ct angiography"):
  with members:
    - "low dose ct image denoising using a generative adversarial network with wasserstein distance and perceptual loss"
    - "framing u net via deep convolutional framelets application to sparse view ct"
    - "a deep convolutional neural network using directional wavelets for low dose x ray ct reconstruction"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "deep convolutional neural network for inverse problems in imaging"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "image to image translation with conditional adversarial networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-5 (induced by source paper "competitive performance of a modularized deep neural network compared to commercial algorithms for low dose ct image reconstruction"):
  with members:
    - "structurally sensitive multi scale deep neural network for low dose ct denoising"
    - "low dose ct image denoising using a generative adversarial network with wasserstein distance and perceptual loss"
    - "image reconstruction by domain transform manifold learning"
    - "3 d convolutional encoder decoder network for low dose ct via transfer learning from a 2 d trained network"
    - "improved training of wasserstein gans"
    - "u net convolutional networks for biomedical image segmentation"
    - "on the difficulty of training recurrent neural networks"

Connected through hyperedge HE-6 (induced by source paper "high resolution mammogram synthesis using progressive generative adversarial networks"):
  with members:
    - "local saddle point optimization a curvature exploitation approach"
    - "synthetic data augmentation using gan for improved liver lesion classification"
    - "can creative adversarial networks generating art by learning about styles and deviating from style norms"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "progressive growing of gans for improved quality stability and variation"
    - "deep image prior"
    - "improved training of wasserstein gans"
    - "stabilizing adversarial nets with prediction methods"

Connected through hyperedge HE-7 (induced by source paper "unsupervised semi supervised deep learning for low dose ct enhancement"):
  with members:
    - "a deep convolutional neural network using directional wavelets for low dose x ray ct reconstruction"
    - "ct image denoising with perceptive deep neural networks"
    - "deep predictive coding networks for video prediction and unsupervised learning"
    - "adam a method for stochastic optimization"

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)