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 "line spectral estimation based on compressed sensing with deterministic sub nyquist sampling"):
  Abstract: As an alternative to the traditional sampling theory, compressed sensing allows acquiring much smaller amount of data, still estimating the spectra of frequency-sparse signals accurately. However, compressed sensing usually requires random sampling in data acquisition, which is difficult to implement in hardware. In this paper, we propose a deterministic and simple sampling scheme, that is, sampling at three sub-Nyquist rates which have coprime undersampled ratios. This sampling method turns out to be valid through numerical experiments. A complex-valued multitask algorithm based on variational Bayesian inference is proposed to estimate the spectra of frequency-sparse signals after sampling. Simulations show that this method is feasible and robust at quite low sampling rates.

Includes members:
  Member "frequency estimation of multiple sinusoids with three sub nyquist channels":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "an algorithm of frequency estimation for multi channel coprime sampling")
  Member "a class of deterministic sensing matrices and their application in harmonic detection":
    also connected through other hyperedges:
      (none)
  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 "on gridless sparse methods for line spectral estimation from complete and incomplete data":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "expectation propagation line spectral estimation")
  Member "super resolution compressed sensing an iterative reweighted algorithm for joint parameter learning and sparse signal recovery":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "sparse methods for direction of arrival estimation")
  Member "variational relevance vector machines":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "probabilistic feature selection and classification vector machine")
  Member "robustly stable signal recovery in compressed sensing with structured matrix perturbation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "fast single image dehazing via multilevel wavelet transform based optimization")
  Member "compressed sensing off the grid":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "approximate support recovery of atomic line spectral estimation a tale of resolution and precision")

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)