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 "variational bayesian inference of line spectral estimation with multiple measurement vectors"):
  Abstract: In this paper, the line spectral estimation (LSE) problem with multiple measurement vectors (MMVs) is studied utilizing the Bayesian methods. Motivated by the recently proposed variational line spectral estimation (VALSE) method, we extend it to deal with the MMVs setting, which is especially important in array signal processing. The VALSE method can automatically estimate the model order and nuisance parameters such as noise variance and weight variance. In addition, by approximating the probability density function (PDF) of the frequencies with the mixture of von Mises PDFs, closed-form update equation and the uncertainty degree of the estimates can be obtained. Interestingly, we find that the VALSE with MMVs can be viewed as applying the VALSE with single measurement vector (SMV) to each snapshot, and combining the intermediate data appropriately. Furthermore, the proposed prior distribution provides a good interpretation of tradeoff between grid and off-grid based methods. Finally, numerical results demonstrate the effectiveness of the VALSE method, compared to the state-of-the-art methods in the MMVs setting.

Includes members:
  Member "grid less variational bayesian inference of line spectral from quantized samples":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multidimensional variational line spectra estimation")
  Member "newtonized orthogonal matching pursuit for line spectrum estimation with multiple measurement vectors":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "grid less variational bayesian inference of line spectral from quantized samples")
  Member "an iterative receiver for ofdm with sparsity based parametric channel estimation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "expectation propagation line spectral estimation")
  Member "variational bayesian inference of line spectra":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "expectation propagation line spectral estimation")
  Member "newtonized orthogonal matching pursuit frequency estimation over the continuum":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep learning based fdd non stationary massive mimo downlink channel reconstruction")
  Member "off the grid line spectrum denoising and estimation with multiple measurement vectors":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "approximate support recovery of atomic line spectral estimation a tale of resolution and precision")
  Member "robust spectral compressed sensing via structured matrix completion":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "recovery analysis of damped spectrally sparse signals and its relation to music")
  Member "continuous compressed sensing with a single or multiple measurement vectors":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "sparse methods for direction of arrival estimation")

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)