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: "a fast algorithm for convolutional structured low rank matrix recovery"
  Abstract: Fourier domain structured low-rank matrix priors are emerging as powerful alternatives to traditional image recovery methods such as total variation and wavelet regularization. These priors specify that a convolutional structured matrix, i.e., Toeplitz, Hankel, or their multi-level generalizations, built from Fourier data of the image should be low-rank. The main challenge in applying these schemes to large-scale problems is the computational complexity and memory demand resulting from lifting the image data to a large scale matrix. We introduce a fast and memory efficient approach called the Generic Iterative Reweighted Annihilation Filter (GIRAF) algorithm that exploits the convolutional structure of the lifted matrix to work in the original un-lifted domain, thus considerably reducing the complexity. Our experiments on the recovery of images from undersampled Fourier measurements show that the resulting algorithm is considerably faster than previously proposed algorithms, and can accommodate much larger problem sizes than previously studied.

Connected through hyperedge HE-1 (induced by source paper "quantitative susceptibility map reconstruction using annihilating filter based low rank hankel matrix approach"):
  with members:
    - "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"
    - "off the grid recovery of piecewise constant images from few fourier samples"

Connected through hyperedge HE-2 (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"
    - "super resolution mri using finite rate of innovation curves"

Connected through hyperedge HE-3 (induced by source paper "modl mussels model based deep learning for multishot sensitivity encoded diffusion mri"):
  with members:
    - "modl model based deep learning architecture for inverse problems"
    - "k space deep learning for reference free epi ghost correction"
    - "learning a variational network for reconstruction of accelerated mri data"
    - "plug and play admm for image restoration fixed point convergence and applications"
    - "u net convolutional networks for biomedical image segmentation"
    - "adam a method for stochastic optimization"

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"
    - "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"
    - "off the grid recovery of piecewise constant images from few fourier samples"

Connected through hyperedge HE-5 (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"
    - "recovery of piecewise smooth images from few fourier samples"
    - "off the grid recovery of piecewise constant 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-6 (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"
    - "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"
    - "off the grid recovery of piecewise constant images from few fourier samples"

Connected through hyperedge HE-7 (induced by source paper "recovery of damped exponentials using structured low rank matrix completion"):
  with members:
    - "novel structured low rank algorithm to recover spatially smooth exponential image time series"
    - "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"
    - "robust spectral compressed sensing via structured matrix completion"

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.NA(Numerical Analysis)