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: "framing u net via deep convolutional framelets application to sparse view ct"
  Abstract: X-ray computed tomography (CT) using sparse projection views is often used to reduce the radiation dose. However, due to the insufficient projection views, a reconstruction approach using the filtered back projection (FBP) produces severe streaking artifacts. Recently, deep learning approaches using large receptive field neural networks such as U-net have demonstrated impressive performance for sparse view CT reconstruction. However, theoretical justification is still lacking. The main goal of this paper is, therefore, to develop a mathematical theory and to discuss how to improve these algorithms. In particular, inspired by the recent theory of deep convolutional framelets, we show that the U-net relies on a sub-optimal non-local bases that overly emphasizes low frequency components. The discovery leads to a dual frame and a tight frame U-net architectures for effective recovery of directional image components.

Connected through hyperedge HE-1 (induced by source paper "a deep learning framework for single sided sound speed inversion in medical ultrasound"):
  with members:
    - "compressed sensing mri reconstruction using a generative adversarial network with a cyclic loss"
    - "low dose ct image denoising using a generative adversarial network with wasserstein distance and perceptual loss"
    - "a benchmark for breast ultrasound image segmentation busis"
    - "3 d convolutional encoder decoder network for low dose ct via transfer learning from a 2 d trained network"
    - "brain tumor segmentation with deep neural networks"
    - "towards ct quality ultrasound imaging using deep learning"
    - "deep learning in rf sub sampled b mode ultrasound imaging"
    - "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases"

Connected through hyperedge HE-2 (induced by source paper "multi level wavelet convolutional neural networks"):
  with members:
    - "parallel grid pooling for data augmentation"
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "image super resolution using very deep residual channel attention networks"
    - "toward convolutional blind denoising of real photographs"
    - "deep laplacian pyramid networks for fast and accurate super resolution"
    - "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration"
    - "enhanced deep residual networks for single image super resolution"
    - "learning deep cnn denoiser prior for image restoration"

Connected through hyperedge HE-3 (induced by source paper "tomogan low dose synchrotron x ray tomography with generative adversarial networks"):
  with members:
    - "a deep journey into super resolution a survey"
    - "denoising imaging polarimetry by adapted bm3d method"
    - "dlhub model and data serving for science"
    - "low dose ct image denoising using a generative adversarial network with wasserstein distance and perceptual loss"
    - "3 d convolutional encoder decoder network for low dose ct via transfer learning from a 2 d trained network"
    - "deep image prior"
    - "deep convolutional neural network for inverse problems in imaging"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-4 (induced by source paper "tomogan low dose x ray tomography with generative adversarial networks"):
  with members:
    - "a deep journey into super resolution a survey"
    - "denoising imaging polarimetry by adapted bm3d method"
    - "dlhub model and data serving for science"
    - "low dose ct image denoising using a generative adversarial network with wasserstein distance and perceptual loss"
    - "3 d convolutional encoder decoder network for low dose ct via transfer learning from a 2 d trained network"
    - "deep convolutional neural network for inverse problems in imaging"
    - "improved training of wasserstein gans"
    - "lose the views limited angle ct reconstruction via implicit sinogram completion"

Connected through hyperedge HE-5 (induced by source paper "fast enhanced ct metal artifact reduction using data domain deep learning"):
  with members:
    - "understanding the disharmony between dropout and batch normalization by variance shift"
    - "cnn based segmentation of medical imaging data"
    - "learning proximal operators using denoising networks for regularizing inverse imaging problems"
    - "understanding the effective receptive field in deep convolutional neural networks"
    - "deep learning for photoacoustic tomography from sparse data"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "deep convolutional neural network for inverse problems in imaging"
    - "can fully convolutional networks perform well for general image restoration problems"

Connected through hyperedge HE-6 (induced by source paper "photorealistic style transfer via wavelet transforms"):
  with members:
    - "a closed form solution to photorealistic image stylization"
    - "arbitrary style transfer with deep feature reshuffle"
    - "nsml meet the mlaas platform with a real world case study"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "scaling the scattering transform deep hybrid networks"
    - "wavelet convolutional neural networks for texture classification"
    - "visual attribute transfer through deep image analogy"
    - "universal style transfer via feature transforms"

Connected through hyperedge HE-7 (induced by source paper "universal deep beamformer for variable rate ultrasound imaging"):
  with members:
    - "a deep learning framework for single sided sound speed inversion in medical ultrasound"
    - "sparse convolutional beamforming for ultrasound imaging"
    - "deep residual learning for accelerated mri using magnitude and phase networks"
    - "high quality ultrasonic multi line transmission through deep learning"
    - "image reconstruction by domain transform manifold learning"
    - "high frame rate cardiac ultrasound imaging with deep learning"
    - "a deep convolutional neural network using directional wavelets for low dose x ray ct reconstruction"
    - "efficient b mode ultrasound image reconstruction from sub sampled rf data using deep learning"

Connected through hyperedge HE-8 (induced by source paper "an end to end deep learning histochemical scoring system for breast cancer tma"):
  with members:
    - "deep label distribution learning with label ambiguity"
    - "detecting cancer metastases on gigapixel pathology images"
    - "not all pixels are equal difficulty aware semantic segmentation via deep layer cascade"
    - "progressive and multi path holistically nested neural networks for pathological lung segmentation from ct images"
    - "deep learning assessment of tumor proliferation in breast cancer histological images"
    - "deep learning for multi task medical image segmentation in multiple modalities"
    - "dcan deep contour aware networks for accurate gland segmentation"
    - "u net convolutional networks for biomedical image segmentation"

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)