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 "structured low rank algorithms theory mr applications and links to machine learning"):
  Abstract: In this survey, we provide a detailed review of recent advances in the recovery of continuous domain multidimensional signals from their few non-uniform (multichannel) measurements using structured low-rank matrix completion formulation. This framework is centered on the fundamental duality between the compactness (e.g., sparsity) of the continuous signal and the rank of a structured matrix, whose entries are functions of the signal. This property enables the reformulation of the signal recovery as a low-rank structured matrix completion, which comes with performance guarantees. We will also review fast algorithms that are comparable in complexity to current compressed sensing methods, which enables the application of the framework to large-scale magnetic resonance (MR) recovery problems. The remarkable flexibility of the formulation can be used to exploit signal properties that are difficult to capture by current sparse and low-rank optimization strategies. We demonstrate the utility of the framework in a wide range of MR imaging (MRI) applications, including highly accelerated imaging, calibration-free acquisition, MR artifact correction, and ungated dynamic MRI.

Includes members:
  Member "understanding geometry of encoder decoder cnns":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "geometric approaches to increase the expressivity of deep neural networks for mr reconstruction")
  Member "free breathing cardiac mri using bandlimited manifold modelling":
    also connected through other hyperedges:
      (none)
  Member "calibration free b0 correction of epi data using structured low rank matrix recovery":
    also connected through other hyperedges:
      (none)
  Member "a fast algorithm for convolutional structured low rank matrix recovery":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "quantitative susceptibility map reconstruction using annihilating filter based low rank hankel matrix approach")
  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 "a general framework for compressed sensing and parallel mri using annihilating filter based low rank hankel matrix":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "geometric approaches to increase the expressivity of deep neural networks for mr reconstruction")
  Member "off the grid recovery of piecewise constant images from few fourier samples":
    also connected through other hyperedges:
      - Hyperedge HE-5 (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.CV(Computer Vision and Pattern Recognition)