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: "novel structured low rank algorithm to recover spatially smooth exponential image time series"
  Abstract: We propose a structured low rank matrix completion algorithm to recover a time series of images consisting of linear combination of exponential parameters at every pixel, from under-sampled Fourier measurements. The spatial smoothness of these parameters is exploited along with the exponential structure of the time series at every pixel, to derive an annihilation relation in the $k-t$ domain. This annihilation relation translates into a structured low rank matrix formed from the $k-t$ samples. We demonstrate the algorithm in the parameter mapping setting and show significant improvement over state of the art methods.

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"
    - "a fast algorithm for convolutional structured low rank matrix recovery"
    - "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-2 (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"
    - "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"
    - "off the grid recovery of piecewise constant images from few fourier samples"

Connected through hyperedge HE-3 (induced by source paper "recovery of damped exponentials using structured low rank matrix completion"):
  with members:
    - "a fast algorithm for convolutional structured low rank matrix recovery"
    - "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.CV(Computer Vision and Pattern Recognition)