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: "recovery of piecewise smooth images from few fourier samples"
  Abstract: We introduce a Prony-like method to recover a continuous domain 2-D piecewise smooth image from few of its Fourier samples. Assuming the discontinuity set of the image is localized to the zero level-set of a trigonometric polynomial, we show the Fourier transform coefficients of partial derivatives of the signal satisfy an annihilation relation. We present necessary and sufficient conditions for unique recovery of piecewise constant images using the above annihilation relation. We pose the recovery of the Fourier coefficients of the signal from the measurements as a convex matrix completion algorithm, which relies on the lifting of the Fourier data to a structured low-rank matrix; this approach jointly estimates the signal and the annihilating filter. Finally, we demonstrate our algorithm on the recovery of MRI phantoms from few low-resolution Fourier samples.

Connected through hyperedge HE-1 (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"
    - "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-2 (induced by source paper "convex recovery of continuous domain piecewise constant images from non uniform fourier samples"):
  with members:
    - "giraf a fast algorithm for 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"
    - "super resolution extremal functions and the condition number of vandermonde matrices"
    - "atomic norm denoising with applications to line spectral estimation"
    - "stable image reconstruction using total variation minimization"
    - "towards a mathematical theory of super resolution"

Connected through hyperedge HE-3 (induced by source paper "a fast algorithm for convolutional structured low rank matrix recovery"):
  with members:
    - "tractable and scalable schatten quasi norm approximations for rank minimization"
    - "weighted schatten p norm minimization for image denoising and background subtraction"
    - "structured low rank recovery of piecewise constant signals with performance guarantees"
    - "compressive sampling using annihilating filter based low rank interpolation"
    - "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"
    - "robust spectral compressed sensing via structured matrix completion"

Connected through hyperedge HE-4 (induced by source paper "structured low rank recovery of piecewise constant signals with performance guarantees"):
  with members:
    - "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"
    - "robust spectral compressed sensing via structured matrix completion"
    - "recovering low rank matrices from few coefficients in any basis"

Connected through hyperedge HE-5 (induced by source paper "off the grid recovery of piecewise constant images from few fourier samples"):
  with members:
    - "a general framework for compressed sensing and parallel mri using annihilating filter based low rank hankel matrix"
    - "robust spectral compressed sensing via structured matrix completion"
    - "near minimax line spectral estimation"
    - "super resolution from noisy data"
    - "towards a mathematical theory of super resolution"

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)