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: "compressive music a missing link between compressive sensing and array signal processing"
  Abstract: The multiple measurement vector (MMV) problem addresses the identification of unknown input vectors that share common sparse support. Even though MMV problems had been traditionally addressed within the context of sensor array signal processing, the recent trend is to apply compressive sensing (CS) due to its capability to estimate sparse support even with an insufficient number of snapshots, in which case classical array signal processing fails. However, CS guarantees the accurate recovery in a probabilistic manner, which often shows inferior performance in the regime where the traditional array signal processing approaches succeed. The apparent dichotomy between the {\em probabilistic} CS and {\em deterministic} sensor array signal processing have not been fully understood. The main contribution of the present article is a unified approach that unveils a {missing link} between CS and array signal processing. The new algorithm, which we call {\em compressive MUSIC}, identifies the parts of support using CS, after which the remaining supports are estimated using a novel generalized MUSIC criterion. Using a large system MMV model, we show that our compressive MUSIC requires a smaller number of sensor elements for accurate support recovery than the existing CS methods and can approach the optimal $l_0$-bound with finite number of snapshots.

Connected through hyperedge HE-1 (induced by source paper "a compact formulation for the ell_ 2 1 mixed norm minimization problem"):
  with members:
    - "off the grid line spectrum denoising and estimation with multiple measurement vectors"
    - "exact joint sparse frequency recovery via optimization methods"
    - "on gridless sparse methods for line spectral estimation from complete and incomplete data"
    - "near minimax line spectral estimation"
    - "atomic norm denoising with applications to line spectral estimation"
    - "compressed sensing off the grid"
    - "super resolution from noisy data"
    - "towards a mathematical theory of super resolution"

Connected through hyperedge HE-2 (induced by source paper "subspace methods for joint sparse recovery"):
  with members:
    - "a probabilistic and ripless theory of compressed sensing"
    - "compressed sensing how sharp is the restricted isometry property"
    - "theoretical and empirical results for recovery from multiple measurements"
    - "model based compressive sensing"
    - "rank awareness in joint sparse recovery"
    - "from theory to practice sub nyquist sampling of sparse wideband analog signals"
    - "construction of a large class of deterministic sensing matrices that satisfy a statistical isometry property"
    - "distributed compressive sensing"

Connected through hyperedge HE-3 (induced by source paper "compressed sensing of simultaneous low rank and joint sparse matrices"):
  with members:
    - "principal component pursuit with reduced linear measurements"
    - "tight oracle bounds for low rank matrix recovery from a minimal number of random measurements"
    - "block sparse signals uncertainty relations and efficient recovery"
    - "rank awareness in joint sparse recovery"
    - "average case analysis of multichannel sparse recovery using convex relaxation"
    - "matrix completion from a few entries"
    - "matrix completion with noise"
    - "robust principal component analysis"

Connected through hyperedge HE-4 (induced by source paper "belief propagation for joint sparse recovery"):
  with members:
    - "asymptotic analysis of map estimation via the replica method and applications to compressed sensing"
    - "subspace methods for joint sparse recovery"
    - "estimation with random linear mixing belief propagation and compressed sensing"
    - "message passing algorithms for compressed sensing"
    - "bayesian compressive sensing via belief propagation"

Connected through hyperedge HE-5 (induced by source paper "bounds on the reconstruction of sparse signal ensembles from distributed measurements"):
  with members:
    - "theoretical and empirical results for recovery from multiple measurements"
    - "model based compressive sensing"
    - "rank awareness in joint sparse recovery"
    - "average case analysis of multichannel sparse recovery using convex relaxation"
    - "on sensing capacity of sensor networks for the class of linear observation fixed snr models"

Connected through hyperedge HE-6 (induced by source paper "compressive music with optimized partial support for joint sparse recovery"):
  with members:
    - "belief propagation for joint sparse recovery"
    - "rank awareness in joint sparse recovery"
    - "necessary and sufficient conditions for sparsity pattern recovery"

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)