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: "sparsity cognizant total least squares for perturbed compressive sampling"
  Abstract: Solving linear regression problems based on the total least-squares (TLS) criterion has well-documented merits in various applications, where perturbations appear both in the data vector as well as in the regression matrix. However, existing TLS approaches do not account for sparsity possibly present in the unknown vector of regression coefficients. On the other hand, sparsity is the key attribute exploited by modern compressive sampling and variable selection approaches to linear regression, which include noise in the data, but do not account for perturbations in the regression matrix. The present paper fills this gap by formulating and solving (regularized) TLS optimization problems under sparsity constraints. Near-optimum and reduced-complexity suboptimum sparse (S-) TLS algorithms are developed to address the perturbed compressive sampling (and the related dictionary learning) challenge, when there is a mismatch between the true and adopted bases over which the unknown vector is sparse. The novel S-TLS schemes also allow for perturbations in the regression matrix of the least-absolute selection and shrinkage selection operator (Lasso), and endow TLS approaches with ability to cope with sparse, under-determined “errors-in-variables” models. Interesting generalizations can further exploit prior knowledge on the perturbations to obtain novel weighted and structured S-TLS solvers. Analysis and simulations demonstrate the practical impact of S-TLS in calibrating the mismatch effects of contemporary grid-based approaches to cognitive radio sensing, and robust direction-of-arrival estimation using antenna arrays.

Connected through hyperedge HE-1 (induced by source paper "bilinear recovery using adaptive vector amp"):
  with members:
    - "regularization by denoising clarifications and new interpretations"
    - "plug in estimation in high dimensional linear inverse problems a rigorous analysis"
    - "near optimal matrix recovery from random linear measurements"
    - "rigorous dynamics of expectation propagation based signal recovery from unitarily invariant measurements"
    - "additivity of information in multilayer networks via additive gaussian noise transforms"
    - "hybrid approximate message passing"
    - "state evolution for approximate message passing with non separable functions"
    - "the replica symmetric prediction for compressed sensing with gaussian matrices is exact"

Connected through hyperedge HE-2 (induced by source paper "quantum annealing based binary compressive sensing with matrix uncertainty"):
  with members:
    - "bilinear adaptive generalized vector approximate message passing"
    - "extending the d wave with support for higher precision coefficients"
    - "nonnegative binary matrix factorization with a d wave quantum annealer"
    - "sharp rip bound for sparse signal and low rank matrix recovery"
    - "general deviants an analysis of perturbations in compressed sensing"

Connected through hyperedge HE-3 (induced by source paper "on radically expanding the landscape of potential applications for automated proof methods"):
  with members:
    - "sign patterns of inverse doubly nonnegative matrices"
    - "on the relative gain array rga with singular and rectangular matrices"

Connected through hyperedge HE-4 (induced by source paper "primary user localization and online radio cartography via structured tensor decomposition"):
  with members:
    - "missing spectrum data recovery in cognitive radio networks using piecewise constant nonnegative matrix factorization"
    - "robustly stable signal recovery in compressed sensing with structured matrix perturbation"

Connected through hyperedge HE-5 (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-6 (induced by source paper "bilinear adaptive generalized vector approximate message passing"):
  with members:
    - "bilinear recovery using adaptive vector amp"
    - "plug in estimation in high dimensional linear inverse problems a rigorous analysis"
    - "hybrid approximate message passing"
    - "bayes optimal joint channel and data estimation for massive mimo with low precision adcs"
    - "parametric bilinear generalized approximate message passing"
    - "phase transitions and sample complexity in bayes optimal matrix factorization"
    - "vector approximate message passing for the generalized linear model"
    - "channel estimation in broadband millimeter wave mimo systems with few bit adcs"

Connected through hyperedge HE-7 (induced by source paper "super resolution mmwave channel estimation using atomic norm minimization"):
  with members:
    - "adaptive interference removal for uncoordinated radar communication coexistence"
    - "vandermonde decomposition of multilevel toeplitz matrices with application to multidimensional super resolution"
    - "hybrid mimo architectures for millimeter wave communications phase shifters or switches"
    - "channel estimation and hybrid precoding for millimeter wave cellular systems"
    - "distributed compressive csit estimation and feedback for fdd multi user massive mimo systems"
    - "spatially sparse precoding in millimeter wave mimo systems"
    - "millimeter wave channel modeling and cellular capacity evaluation"
    - "a remark on the restricted isometry property in orthogonal matching pursuit"

Connected through hyperedge HE-8 (induced by source paper "superresolution method for data deconvolution by superposition of point sources"):
  with members:
    - "super resolution of positive sources the discrete setup"

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)