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: "a stochastic gradient approach on compressive sensing signal reconstruction based on adaptive filtering framework"
  Abstract: Based on the methodological similarity between sparse signal reconstruction and system identification, a new approach for sparse signal reconstruction in compressive sensing (CS) is proposed in this paper. This approach employs a stochastic gradient-based adaptive filtering framework, which is commonly used in system identification, to solve the sparse signal reconstruction problem. Two typical algorithms for this problem: l 0-least mean square ( l 0-LMS) algorithm and l 0-exponentially forgetting window LMS (l 0-EFWLMS) algorithm are hence introduced here. Both the algorithms utilize a zero attraction method, which has been implemented by minimizing a continuous approximation of l 0 norm of the studied signal. To improve the performances of these proposed algorithms, an l 0-zero attraction projection (l 0 -ZAP) algorithm is also adopted, which has effectively accelerated their convergence rates, making them much faster than the other existing algorithms for this problem. Advantages of the proposed approach, such as its robustness against noise, etc., are demonstrated by numerical experiments.

Connected through hyperedge HE-1 (induced by source paper "recursive recovery of sparse signal sequences from compressive measurements a review"):
  with members:
    - "video compressive sensing for spatial multiplexing cameras using motion flow models"
    - "phase retrieval via wirtinger flow theory and algorithms"
    - "fpa cs focal plane array based compressive imaging in short wave infrared"
    - "quadratic basis pursuit"
    - "estimation of space time varying parameters using a diffusion lms algorithm"
    - "an online algorithm for separating sparse and low dimensional signal sequences from their sum"
    - "sparse recovery of streaming signals using l1 homotopy"
    - "lensless imaging by compressive sensing"

Connected through hyperedge HE-2 (induced by source paper "robustness of sparse recovery via f minimization a topological viewpoint"):
  with members:
    - "living on the edge phase transitions in convex programs with random data"
    - "approximate projected generalized gradient methods with sparsity inducing penalties"
    - "the convergence guarantees of a non convex approach for sparse recovery"
    - "proof of convergence and performance analysis for sparse recovery via zero point attracting projection"
    - "optimal phase transitions in compressed sensing"
    - "compressed sensing how sharp is the restricted isometry property"
    - "on the stable recovery of the sparsest overcomplete representations in presence of noise"
    - "a fast approach for overcomplete sparse decomposition based on smoothed ell 0 norm"

Connected through hyperedge HE-3 (induced by source paper "an improved variable step size zero point attracting projection algorithm"):
  with members:
    - "a new variable step size zero point attracting projection algorithm"
    - "proof of convergence and performance analysis for sparse recovery via zero point attracting projection"
    - "performance analysis of l_0 norm constraint least mean square algorithm"
    - "l_ 0 norm constraint lms algorithm for sparse system identification"

Connected through hyperedge HE-4 (induced by source paper "maximum correntropy criterion based sparse adaptive filtering algorithms for robust channel estimation under non gaussian environments"):
  with members:
    - "diffusion least mean p power algorithms for distributed estimation in alpha stable noise environments"
    - "variable earns profit improved adaptive channel estimation using sparse vss nlms algorithms"

Connected through hyperedge HE-5 (induced by source paper "on the performance bound of sparse estimation with sensing matrix perturbation"):
  with members:
    - "perturbation analysis of orthogonal matching pursuit"
    - "the pros and cons of compressive sensing for wideband signal acquisition noise folding versus dynamic range"
    - "proof of convergence and performance analysis for sparse recovery via zero point attracting projection"
    - "sparsity cognizant total least squares for perturbed compressive sampling"
    - "noise folding in compressed sensing"
    - "how well can we estimate a sparse vector"
    - "general deviants an analysis of perturbations in compressed sensing"
    - "compressed sensing how sharp is the restricted isometry property"

Connected through hyperedge HE-6 (induced by source paper "oracle order recovery performance of greedy pursuits with replacement against general perturbations"):
  with members:
    - "perturbation analysis of orthogonal matching pursuit"
    - "proof of convergence and performance analysis for sparse recovery via zero point attracting projection"
    - "sparsity cognizant total least squares for perturbed compressive sampling"
    - "improved bounds on restricted isometry constants for gaussian matrices"
    - "general deviants an analysis of perturbations in compressed sensing"
    - "compressed sensing how sharp is the restricted isometry property"
    - "compressed sensing performance bounds under poisson noise"
    - "new bounds for restricted isometry constants"

Connected through hyperedge HE-7 (induced by source paper "a new variable step size zero point attracting projection algorithm"):
  with members:
    - "proof of convergence and performance analysis for sparse recovery via zero point attracting projection"
    - "performance analysis of l_0 norm constraint least mean square algorithm"
    - "l_ 0 norm constraint lms algorithm for sparse system identification"

Connected through hyperedge HE-8 (induced by source paper "sparsity aware stap algorithms using l_1 norm regularization for radar systems"):
  with members:
    - "registration based compensation using sparse representation in conformal array stap"
    - "direct data domain stap using sparse representation of clutter spectrum"
    - "airborne radar stap using sparse recovery of clutter spectrum"

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)