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: "super resolution compressed sensing an iterative reweighted algorithm for joint parameter learning and sparse signal recovery"
  Abstract: In many practical applications such as direction-of- arrival (DOA) estimation and line spectral estimation, the sparsifying dictionary is usually characterized by a set of unknown parameters in a continuous domain. To apply the conventional compressed sensing to such applications, the continuous parameter space has to be discretized to a finite set of grid points. Discretization, however, incurs errors and leads to deteriorated recovery performance. To address this issue, we propose an iterative reweighted method which jointly estimates the unknown parameters and the sparse signals. Specifically, the proposed algorithm is developed by iteratively decreasing a surrogate function majorizing a given objective function, which results in a gradual and interweaved iterative process to refine the unknown parameters and the sparse signal. Numerical results show that the algorithm provides superior performance in resolving closely-spaced frequency components.

Connected through hyperedge HE-1 (induced by source paper "sparse methods for direction of arrival estimation"):
  with members:
    - "a compact formulation for the ell_ 2 1 mixed norm minimization problem"
    - "fast and provable algorithms for spectrally sparse signal reconstruction via low rank hankel matrix completion"
    - "an overview of low rank matrix recovery from incomplete observations"
    - "robust recovery of complex exponential signals from random gaussian projections via low rank hankel matrix reconstruction"
    - "vandermonde decomposition of multilevel toeplitz matrices with application to multidimensional super resolution"
    - "off the grid line spectrum denoising and estimation with multiple measurement vectors"
    - "robust spectral compressed sensing via structured matrix completion"
    - "exact joint sparse frequency recovery via optimization methods"

Connected through hyperedge HE-2 (induced by source paper "channel estimation for tdd fdd massive mimo systems with channel covariance computing"):
  with members:
    - "an overview of signal processing techniques for millimeter wave mimo systems"
    - "low rank covariance assisted downlink training and channel estimation for fdd massive mimo systems"
    - "channel covariance estimation in massive mimo frequency division duplex systems"
    - "spatially common sparsity based adaptive channel estimation and feedback for fdd massive mimo"
    - "pilot reuse for massive mimo transmission over spatially correlated rayleigh fading channels"
    - "channel estimation and hybrid precoding for millimeter wave cellular systems"
    - "distributed compressive csit estimation and feedback for fdd multi user massive mimo systems"
    - "massive mimo for next generation wireless systems"

Connected through hyperedge HE-3 (induced by source paper "line spectral estimation based on compressed sensing with deterministic sub nyquist sampling"):
  with members:
    - "frequency estimation of multiple sinusoids with three sub nyquist channels"
    - "a class of deterministic sensing matrices and their application in harmonic detection"
    - "robust spectral compressed sensing via structured matrix completion"
    - "on gridless sparse methods for line spectral estimation from complete and incomplete data"
    - "variational relevance vector machines"
    - "robustly stable signal recovery in compressed sensing with structured matrix perturbation"
    - "compressed sensing off the grid"
    - "towards a mathematical theory of super resolution"

Connected through hyperedge HE-4 (induced by source paper "distributed compressed sensing off the grid"):
  with members:
    - "super resolution line spectrum estimation with block priors"
    - "music for single snapshot spectral estimation stability and super resolution"
    - "direction of arrival estimation using co prime arrays a super resolution viewpoint"
    - "off the grid spectral compressed sensing with prior information"
    - "spectral compressive sensing with polar interpolation"
    - "spectral compressed sensing via structured matrix completion"
    - "joint sparsity recovery for spectral compressed sensing"
    - "spectral compressive sensing with model selection"

Connected through hyperedge HE-5 (induced by source paper "gridless quadrature compressive sampling with interpolated array technique"):
  with members:
    - "robust spectral compressed sensing via structured matrix completion"
    - "music for single snapshot spectral estimation stability and super resolution"
    - "quadrature compressive sampling for radar signals"
    - "joint sparse recovery method for compressed sensing with structured dictionary mismatches"
    - "compressed sensing off the grid"
    - "towards a mathematical theory of super resolution"
    - "sparsity cognizant total least squares for perturbed compressive sampling"
    - "coherence pattern guided compressive sensing with unresolved grids"

Connected through hyperedge HE-6 (induced by source paper "exact joint sparse frequency recovery via optimization methods"):
  with members:
    - "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"
    - "a discretization free sparse and parametric approach for linear array signal processing"
    - "direction of arrival estimation using co prime arrays a super resolution viewpoint"
    - "on gridless sparse methods for line spectral estimation from complete and incomplete data"
    - "near minimax line spectral estimation"
    - "joint sparsity recovery for spectral compressed sensing"

Connected through hyperedge HE-7 (induced by source paper "super resolution compressed sensing a generalized iterative reweighted l2 approach"):
  with members:
    - "near minimax line spectral estimation"
    - "pattern coupled sparse bayesian learning for recovery of block sparse signals"
    - "compressed sensing off the grid"
    - "towards a mathematical theory of super resolution"
    - "coherence pattern guided compressive sensing with unresolved grids"
    - "beyond nyquist efficient sampling of sparse bandlimited signals"

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)