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: "compressive sampling using annihilating filter based low rank interpolation"
  Abstract: While the recent theory of compressed sensing provides an opportunity to overcome the Nyquist limit in recovering sparse signals, a solution approach usually takes a form of inverse problem of the unknown signal, which is crucially dependent on specific signal representation. In this paper, we propose a drastically different two-step Fourier compressive sampling framework in continuous domain that can be implemented as a measurement domain interpolation, after which a signal reconstruction can be done using classical analytic reconstruction methods. The main idea is originated from the fundamental duality between the sparsity in the primary space and the low-rankness of a structured matrix in the spectral domain, which shows that a low-rank interpolator in the spectral domain can enjoy all the benefit of sparse recovery with performance guarantees. Most notably, the proposed low-rank interpolation approach can be regarded as a generalization of recent spectral compressed sensing to recover large class of finite rate of innovations (FRI) signals at near optimal sampling rate. Moreover, for the case of cardinal representation, we can show that the proposed low-rank interpolation will benefit from inherent regularization and the optimal incoherence parameter. Using the powerful dual certificates and golfing scheme, we show that the new framework still achieves the near-optimal sampling rate for general class of FRI signal recovery, and the sampling rate can be further reduced for the class of cardinal splines. Numerical results using various type of FRI signals confirmed that the proposed low-rank interpolation approach has significant better phase transition than the conventional CS approaches.

Connected through hyperedge HE-1 (induced by source paper "quantitative susceptibility map reconstruction using annihilating filter based low rank hankel matrix approach"):
  with members:
    - "a fast algorithm for convolutional structured low rank matrix recovery"
    - "a general framework for compressed sensing and parallel mri using annihilating filter based low rank hankel matrix"
    - "off the grid recovery of piecewise constant images from few fourier samples"

Connected through hyperedge HE-2 (induced by source paper "structured low rank algorithms theory mr applications and links to machine learning"):
  with members:
    - "understanding geometry of encoder decoder cnns"
    - "free breathing cardiac mri using bandlimited manifold modelling"
    - "calibration free b0 correction of epi data using structured low rank matrix recovery"
    - "a fast algorithm for convolutional structured low rank matrix recovery"
    - "a general framework for compressed sensing and parallel mri using annihilating filter based low rank hankel matrix"
    - "off the grid recovery of piecewise constant images from few fourier samples"

Connected through hyperedge HE-3 (induced by source paper "deep residual learning for accelerated mri using magnitude and phase networks"):
  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"
    - "deep generative adversarial networks for compressed sensing automates mri"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "deep convolutional neural network for inverse problems in imaging"
    - "learning a variational network for reconstruction of accelerated mri data"
    - "photo realistic single image super resolution using a generative adversarial network"

Connected through hyperedge HE-4 (induced by source paper "k space deep learning for accelerated mri"):
  with members:
    - "understanding geometry of encoder decoder cnns"
    - "compressed sensing mri reconstruction using a generative adversarial network with a cyclic loss"
    - "deep residual learning for accelerated mri using magnitude and phase networks"
    - "image reconstruction by domain transform manifold learning"
    - "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"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "deep convolutional neural network for inverse problems in imaging"

Connected through hyperedge HE-5 (induced by source paper "k space deep learning for reference free epi ghost correction"):
  with members:
    - "understanding geometry of encoder decoder cnns"
    - "navigator free epi ghost correction with structured low rank matrix models new theory and methods"
    - "u net convolutional networks for biomedical image segmentation"
    - "a general framework for compressed sensing and parallel mri using annihilating filter based low rank hankel matrix"
    - "recovering low rank matrices from few coefficients in any basis"
    - "the power of convex relaxation near optimal matrix completion"
    - "matrix completion from a few entries"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-6 (induced by source paper "k space deep learning for parallel mri application to time resolved mr angiography"):
  with members:
    - "deep residual learning for accelerated mri using magnitude and phase networks"
    - "k space deep learning for accelerated mri"
    - "u net convolutional networks for biomedical image segmentation"
    - "a general framework for compressed sensing and parallel mri using annihilating filter based low rank hankel matrix"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "mulan a blind and off grid method for multichannel echo retrieval"):
  with members:
    - "separake source separation with a little help from echoes"
    - "guaranteed blind sparse spikes deconvolution via lifting and convex optimization"
    - "raking the cocktail party"

Connected through hyperedge HE-8 (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"
    - "ct image denoising with perceptive deep neural networks"
    - "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"

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.IT(Information Theory)