human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "bayesian compressive sensing approaches for direction of arrival estimation with mutual coupling effects"
  Abstract: The problem of estimating the dynamic direction of arrival (DOA) of far-field signals impinging on a uniform linear array, with mutual coupling effects, is addressed. This paper proposes two novel approaches able to provide accurate solutions, including at the endfire regions of the array. First, a Bayesian compressive sensing Kalman filter is developed, which accounts for the predicted estimated signals rather than using the traditional sparse prior. The posterior probability density function of the received source signals and the expression for the related marginal likelihood function are derived theoretically. Next, a Gibbs sampling-based approach with indicator variables in the sparsity prior is developed. This allows sparsity to be explicitly enforced in different ways, including when an angle is too far from the previous estimate. The proposed approaches are validated and evaluated over different test scenarios and compared to the traditional relevance vector machine (RVM)-based method. An improved accuracy in terms of average root-mean-square error values is achieved (up to 73.39% for the modified RVM-based approach and 86.36% for the Gibbs sampling-based approach). The proposed approaches prove to be particularly useful for DOA estimation when the angle of arrival moves into the endfire region of the array.

Connected through hyperedge HE-1 (induced by source paper "bayesian inference for pca and music algorithms with unknown number of sources"):
  with members:
    - "root sparse bayesian learning for off grid doa estimation"
    - "variational bayesian inference of line spectra"
    - "enhancing sparsity and resolution via reweighted atomic norm minimization"
    - "near minimax line spectral estimation"
    - "atomic norm denoising with applications to line spectral estimation"

Question: Please tell me which class the center node 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)