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 "off the grid line spectrum denoising and estimation with multiple measurement vectors"):
  Abstract: Compressed Sensing suggests that the required number of samples for reconstructing a signal can be greatly reduced if it is sparse in a known discrete basis, yet many real-world signals are sparse in a continuous dictionary. One example is the spectrally-sparse signal, which is composed of a small number of spectral atoms with arbitrary frequencies on the unit interval. In this paper we study the problem of line spectrum denoising and estimation with an ensemble of spectrally-sparse signals composed of the same set of continuous-valued frequencies from their partial and noisy observations. Two approaches are developed based on atomic norm minimization and structured covariance estimation, both of which can be solved efficiently via semidefinite programming. The first approach aims to estimate and denoise the set of signals from their partial and noisy observations via atomic norm minimization, and recover the frequencies via examining the dual polynomial of the convex program. We characterize the optimality condition of the proposed algorithm and derive the expected convergence rate for denoising, demonstrating the benefit of including multiple measurement vectors. The second approach aims to recover the population covariance matrix from the partially observed sample covariance matrix by motivating its low-rank Toeplitz structure without recovering the signal ensemble. Performance guarantee is derived with a finite number of measurement vectors. The frequencies can be recovered via conventional spectrum estimation methods such as MUSIC from the estimated covariance matrix. Finally, numerical examples are provided to validate the favorable performance of the proposed algorithms, with comparisons against several existing approaches.

Includes members:
  Member "compressive parameter estimation for sparse translation invariant signals using polar interpolation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "compressive parameter estimation via k median clustering")
  Member "robust spectral compressed sensing via structured matrix completion":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "recovery analysis of damped spectrally sparse signals and its relation to music")
  Member "exact joint sparse frequency recovery via optimization methods":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "approximate support recovery of atomic line spectral estimation a tale of resolution and precision")
  Member "a discretization free sparse and parametric approach for linear array signal processing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "expectation propagation line spectral estimation")
  Member "spectral compressed sensing via structured matrix completion":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "two dimensional super resolution via convex relaxation")
  Member "joint sparsity recovery for spectral compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "super resolution blind channel and signal estimation for massive mimo with one dimensional antenna array")
  Member "precise semidefinite programming formulation of atomic norm minimization for recovering d dimensional d geq 2 off the grid frequencies":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "super resolution on the sphere using convex optimization")
  Member "atomic norm denoising with applications to line spectral estimation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "convolutional dictionary learning based auto encoders for natural exponential family distributions")

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)