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 "convex recovery of continuous domain piecewise constant images from non uniform fourier samples"):
  Abstract: We consider the recovery of a continuous domain piecewise constant image from its non-uniform Fourier samples using a convex matrix completion algorithm. We assume the discontinuities/edges of the image are localized to the zero levelset of a bandlimited function. This assumption induces linear dependencies between the Fourier coefficients of the image, which results in a two-fold block Toeplitz matrix constructed from the Fourier coefficients being low-rank. The proposed algorithm reformulates the recovery of the unknown Fourier coefficients as a structured low-rank matrix completion problem, where the nuclear norm of the matrix is minimized subject to structure and data constraints. We show that exact recovery is possible with high probability when the edge set of the image satisfies an incoherency property. We also show that the incoherency property is dependent on the geometry of the edge set curve, implying higher sampling burden for smaller curves. This paper generalizes recent work on the super-resolution recovery of isolated Diracs or signals with finite rate of innovation to the recovery of piecewise constant images.

Includes members:
  Member "giraf a fast algorithm for structured low rank matrix recovery":
    also connected through other hyperedges:
      (none)
  Member "compressive sampling using annihilating filter based low rank interpolation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "quantitative susceptibility map reconstruction using annihilating filter based low rank hankel matrix approach")
  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-2 (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-3 (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-4 (induced by source paper "quantitative susceptibility map reconstruction using annihilating filter based low rank hankel matrix approach")
  Member "super resolution extremal functions and the condition number of vandermonde matrices":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "resolution limit for line spectral estimation theory and algorithm")
  Member "atomic norm denoising with applications to line spectral estimation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "convolutional dictionary learning based auto encoders for natural exponential family distributions")
  Member "stable image reconstruction using total variation minimization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "compressed sensing with 1d total variation breaking sample complexity barriers via non uniform recovery")

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.IT(Information Theory)