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: "variational bayesian inference of line spectra"
  Abstract: We address the fundamental problem of line spectral estimation in a Bayesian framework. We target model order and parameter estimation via variational inference in a probabilistic model in which the frequencies are continuous-valued, i.e., not restricted to a grid; and the coefficients are governed by a Bernoulli-Gaussian prior model turning model order selection into binary sequence detection. Unlike earlier works which retain only point estimates of the frequencies, we undertake a complete Bayesian treatment by estimating the posterior probability density functions (pdfs) of the frequencies and computing expectations over them. Thus, we additionally capture and operate with the uncertainty of the frequency estimates. Aiming to maximize the model evidence, variational optimization provides analytic approximations of the posterior pdfs and also gives estimates of the additional parameters. We propose an accurate representation of the frequency pdfs by mixtures of von Mises pdfs, which yields closed-form expectations. We define the algorithm VALSE in which the estimates of the pdfs and parameters are iteratively updated. VALSE is a gridless, convergent method, does not require parameter tuning, can easily include prior knowledge about the frequencies and provides approximate posterior pdfs based on which the uncertainty in line spectral estimation can be quantified. Simulation results show that accounting for the uncertainty of frequency estimates, rather than computing just point estimates, significantly improves the performance. The performance of VALSE is superior to that of state-of-the-art methods and closely approaches the Cramer-Rao bound computed for the true model order.

Connected through hyperedge HE-1 (induced by source paper "expectation propagation line spectral estimation"):
  with members:
    - "a unified bayesian inference framework for generalized linear models"
    - "bilinear adaptive generalized vector approximate message passing"
    - "sparse methods for direction of arrival estimation"
    - "multi snapshot newtonized orthogonal matching pursuit for line spectrum estimation with multiple measurement vectors"
    - "an iterative receiver for ofdm with sparsity based parametric channel estimation"
    - "newtonized orthogonal matching pursuit frequency estimation over the continuum"
    - "off the grid line spectrum denoising and estimation with multiple measurement vectors"
    - "robust spectral compressed sensing via structured matrix completion"

Connected through hyperedge HE-2 (induced by source paper "a belief propagation algorithm for multipath based slam"):
  with members:
    - "generalized optimal sub pattern assignment metric"
    - "a scalable algorithm for tracking an unknown number of targets using multiple sensors"
    - "cooperative simultaneous localization and synchronization in mobile agent networks"
    - "marginal multi bernoulli filters rfs derivation of mht jipda and association based member"
    - "approximate evaluation of marginal association probabilities with belief propagation"
    - "evaluation of position related information in multipath components for indoor positioning"

Connected through hyperedge HE-3 (induced by source paper "multidimensional variational line spectra estimation"):
  with members:
    - "grid less variational bayesian inference of line spectral from quantized samples"
    - "efficient downlink channel reconstruction for fdd multi antenna systems"
    - "compressive channel estimation and tracking for large arrays in mm wave picocells"
    - "vandermonde decomposition of multilevel toeplitz matrices with application to multidimensional super resolution"
    - "robust spectral compressed sensing via structured matrix completion"

Connected through hyperedge HE-4 (induced by source paper "grid less variational bayesian inference of line spectral from quantized samples"):
  with members:
    - "a unified bayesian inference framework for generalized linear models"
    - "variational bayesian inference of line spectral estimation with multiple measurement vectors"
    - "bilinear adaptive generalized vector approximate message passing"
    - "sparse methods for direction of arrival estimation"
    - "newtonized orthogonal matching pursuit for line spectrum estimation with multiple measurement vectors"
    - "hybrid approximate message passing"
    - "newtonized orthogonal matching pursuit frequency estimation over the continuum"
    - "vector approximate message passing for the generalized linear model"

Connected through hyperedge HE-5 (induced by source paper "variational bayesian inference of line spectral estimation with multiple measurement vectors"):
  with members:
    - "grid less variational bayesian inference of line spectral from quantized samples"
    - "newtonized orthogonal matching pursuit for line spectrum estimation with multiple measurement vectors"
    - "an iterative receiver for ofdm with sparsity based parametric channel estimation"
    - "newtonized orthogonal matching pursuit frequency estimation over the continuum"
    - "off the grid line spectrum denoising and estimation with multiple measurement vectors"
    - "robust spectral compressed sensing via structured matrix completion"
    - "continuous compressed sensing with a single or multiple measurement vectors"
    - "enhancing sparsity and resolution via reweighted atomic norm minimization"

Connected through hyperedge HE-6 (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"
    - "bayesian compressive sensing approaches for direction of arrival estimation with mutual coupling effects"
    - "enhancing sparsity and resolution via reweighted atomic norm minimization"
    - "near minimax line spectral estimation"
    - "atomic norm denoising with applications to line spectral estimation"

Connected through hyperedge HE-7 (induced by source paper "a scalable belief propagation algorithm for radio signal based slam"):
  with members:
    - "a scalable algorithm for tracking an unknown number of targets using multiple sensors"
    - "approximate evaluation of marginal association probabilities with belief propagation"
    - "evaluation of position related information in multipath components for indoor positioning"

Connected through hyperedge HE-8 (induced by source paper "joint sparse channel estimation and decoding continuous and discrete domain sparsity"):
  with members:
    - "atomic norm denoising with applications to line spectral estimation"
    - "fast and robust parametric estimation of jointly sparse channels"
    - "message passing algorithms for channel estimation and decoding using approximate inference"
    - "towards a mathematical theory of super resolution"
    - "a message passing receiver for bicm ofdm over unknown clustered sparse channels"
    - "merging belief propagation and the mean field approximation a free energy approach"
    - "compressive estimation of doubly selective channels in multicarrier systems leakage effects and sparsity enhancing processing"
    - "a message passing approach for joint channel estimation interference mitigation and decoding"

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)