human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "tractable and scalable schatten quasi norm approximations for rank minimization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "efficient relaxed gradient support pursuit for sparsity constrained non convex optimization")
  Member "weighted schatten p norm minimization for image denoising and background subtraction":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a benchmark for sparse coding when group sparsity meets rank minimization")
  Member "structured low rank recovery of piecewise constant signals with performance guarantees":
    also connected through other hyperedges:
      (none)
  Member "compressive sampling using annihilating filter based low rank interpolation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "quantitative susceptibility map reconstruction using annihilating filter based low rank hankel matrix approach")
  Member "super resolution mri using finite rate of innovation curves":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "modl model based deep learning architecture for inverse problems")
  Member "a general framework for compressed sensing and parallel mri using annihilating filter based low rank hankel matrix":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "geometric approaches to increase the expressivity of deep neural networks for mr reconstruction")
  Member "recovery of piecewise smooth images from few fourier samples":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "recovery of noisy points on band limited surfaces kernel methods re explained")
  Member "off the grid recovery of piecewise constant images from few fourier samples":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "quantitative susceptibility map reconstruction using annihilating filter based low rank hankel matrix approach")

Question: Please tell me which class the source paper 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)