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: "off the grid recovery of piecewise constant images from few fourier samples"
  Abstract: We introduce a method to recover a continuous domain representation of a piecewise constant two-dimensional image from few low-pass Fourier samples. Assuming the edge set of the image is localized to the zero set of a trigonometric polynomial, we show the Fourier coefficients of the partial derivatives of the image satisfy a linear annihilation relation. We present necessary and sufficient conditions for unique recovery of the image from finite low-pass Fourier samples using the annihilation relation. We also propose a practical two-stage recovery algorithm which is robust to model-mismatch and noise. In the first stage we estimate a continuous domain representation of the edge set of the image. In the second stage we perform an extrapolation in Fourier domain by a least squares two-dimensional linear prediction, which recovers the exact Fourier coefficients of the underlying image. We demonstrate our algorithm on the super-resolution recovery of MRI phantoms and real MRI data from low-pass Fourier samples, which shows benefits over standard approaches for single-image super-resolution MRI.

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"
    - "compressive sampling using annihilating filter based low rank interpolation"
    - "a general framework for compressed sensing and parallel mri using annihilating filter based low rank hankel matrix"

Connected through hyperedge HE-2 (induced by source paper "modl model based deep learning architecture for inverse problems"):
  with members:
    - "model based free breathing cardiac mri reconstruction using deep learned storm priors modl storm"
    - "a deep convolutional neural network using directional wavelets for low dose x ray ct reconstruction"
    - "learning deep cnn denoiser prior for image restoration"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "deep convolutional neural network for inverse problems in imaging"
    - "unrolled optimization with deep priors"
    - "recurrent generative adversarial networks for proximal learning and automated compressive image recovery"
    - "recurrent inference machines for solving inverse problems"

Connected through hyperedge HE-3 (induced by source paper "deep generalization of structured low rank algorithms deep slr"):
  with members:
    - "modl model based deep learning architecture for inverse problems"
    - "structured low rank algorithms theory mr applications and links to machine learning"
    - "deep residual learning for accelerated mri using magnitude and phase networks"
    - "off the grid model based deep learning o modl"
    - "k space deep learning for accelerated mri"
    - "deep convolutional neural network for inverse problems in imaging"
    - "learning a variational network for reconstruction of accelerated mri data"
    - "a fast algorithm for convolutional structured low rank matrix recovery"

Connected through hyperedge HE-4 (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"
    - "compressive sampling using annihilating filter based low rank interpolation"
    - "a general framework for compressed sensing and parallel mri using annihilating filter based low rank hankel matrix"

Connected through hyperedge HE-5 (induced by source paper "sredgenet edge enhanced single image super resolution using dense edge detection network and feature merge network"):
  with members:
    - "deeply supervised salient object detection with short connections"
    - "connecting image denoising and high level vision tasks via deep learning"
    - "residual dense network for image super resolution"
    - "deep edge guided recurrent residual learning for image super resolution"
    - "richer convolutional features for edge detection"
    - "enhanced deep residual networks for single image super resolution"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "holistically nested edge detection"

Connected through hyperedge HE-6 (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-7 (induced by source paper "recovery of noisy points on band limited surfaces kernel methods re explained"):
  with members:
    - "recovery of damped exponentials using structured low rank matrix completion"
    - "convex recovery of continuous domain piecewise constant images from non uniform fourier samples"
    - "novel structured low rank algorithm to recover spatially smooth exponential image time series"
    - "a fast algorithm for convolutional structured low rank matrix recovery"
    - "recovery of piecewise smooth images from few fourier samples"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"

Connected through hyperedge HE-8 (induced by source paper "calibration free b0 correction of epi data using structured low rank matrix recovery"):
  with members:
    - "recovery of damped exponentials using structured low rank matrix completion"
    - "navigator free epi ghost correction with structured low rank matrix models new theory and methods"
    - "novel structured low rank algorithm to recover spatially smooth exponential image time series"
    - "a fast algorithm for convolutional structured low rank matrix recovery"
    - "super resolution mri using finite rate of innovation curves"
    - "a general framework for compressed sensing and parallel mri using annihilating filter based low rank hankel matrix"

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)