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 "expectation propagation line spectral estimation"):
  Abstract: Line spectral estimation (LSE) is a fundamental problem in signal processing fields, as it arises in various fields such as radar signal processing and communication fields. This paper develops expectation propagation (EP) based LSE (EPLSE) method. The proposed method automatically estimates the model order, noise variance, and can deal with the nonlinear measurements. Numerical experiments show the excellent performance of EPLSE.

Includes members:
  Member "a unified bayesian inference framework for generalized linear models":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a new insight into gamp and amp")
  Member "bilinear adaptive generalized vector approximate message passing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a new insight into gamp and amp")
  Member "sparse methods for direction of arrival estimation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "grid less variational bayesian inference of line spectral from quantized samples")
  Member "multi snapshot newtonized orthogonal matching pursuit for line spectrum estimation with multiple measurement vectors":
    also connected through other hyperedges:
      (none)
  Member "an iterative receiver for ofdm with sparsity based parametric channel estimation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "variational bayesian inference of line spectral estimation with multiple measurement vectors")
  Member "variational bayesian inference of line spectra":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a belief propagation algorithm for multipath based slam")
  Member "newtonized orthogonal matching pursuit frequency estimation over the continuum":
    also connected through other hyperedges:
      - Hyperedge HE-6 (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-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)