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 "sparse methods for direction of arrival estimation"):
  Abstract: Abstract   Direction-of-arrival (DOA) estimation refers to the process of retrieving the direction information of several electromagnetic waves/sources from the outputs of a number of receiving antennas that form a sensor array. DOA estimation is a major problem in array signal processing and has wide applications in radar, sonar, wireless communications, etc. With the development of sparse representation and compressed sensing, the last decade has witnessed a tremendous advance in this research topic. The purpose of this article is to provide an overview of these sparse methods for DOA estimation, with a particular highlight on the recently developed gridless sparse methods, e.g., those based on covariance fitting and the atomic norm. Several future research directions are also discussed.

Includes members:
  Member "a compact formulation for the ell_ 2 1 mixed norm minimization problem":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "block and rank sparse recovery for direction finding in partly calibrated arrays")
  Member "fast and provable algorithms for spectrally sparse signal reconstruction via low rank hankel matrix completion":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "exact matrix completion based on low rank hankel structure in the fourier domain")
  Member "an overview of low rank matrix recovery from incomplete observations":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "bilinear recovery using adaptive vector amp")
  Member "robust recovery of complex exponential signals from random gaussian projections via low rank hankel matrix reconstruction":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "atomic norm denoising for complex exponentials with unknown waveform modulations")
  Member "vandermonde decomposition of multilevel toeplitz matrices with application to multidimensional super resolution":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multi dimensional spectral super resolution with prior knowledge via frequency selective vandermonde decomposition and admm")
  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 "exact joint sparse frequency recovery via optimization methods":
    also connected through other hyperedges:
      - Hyperedge HE-8 (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)