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: "performance analysis of l_0 norm constraint least mean square algorithm"
  Abstract: As one of the recently proposed algorithms for sparse system identification, I0 norm constraint Least Mean Square (io-LMS) algorithm modifies the cost function of the traditional method with a penalty of tap-weight sparsity. The performance of I0-LMS is quite attractive compared with its various precursors. However, there has been no detailed study of its performance. This paper presents comprehensive theoretical performance analysis of I0-LMS for white Gaussian input data based on some reasonable assumptions, which are reasonable in a large range of parameter setting. Expressions for steady-state mean square deviation (MSD) are derived and discussed with respect to algorithm parameters and system sparsity. The parameter selection rule is established for achieving the best performance. Approximated with Taylor series, the instantaneous behavior is also derived. In addition, the relationship between I0-LMS and some previous arts and the sufficient conditions for I0-LMS to accelerate convergence are set up. Finally, all of the theoretical results are compared with simulations and are shown to agree well in a wide range of parameters.

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 "transient performance analysis of zero attracting lms"):
  with members:
    - "steady state performance of non negative least mean square algorithm and its variants"

Connected through hyperedge HE-3 (induced by source paper "block sparsity induced adaptive filter for multi clustering system identification"):
  with members:
    - "reweighted l1 norm penalized lms for sparse channel estimation and its analysis"
    - "efficient recovery of block sparse signals via zero point attracting projection"
    - "near oracle performance of greedy block sparse estimation techniques from noisy measurements"
    - "block sparse signals uncertainty relations and efficient recovery"
    - "sparse recovery using smoothed ell 0 sl0 convergence analysis"
    - "model based compressive sensing"
    - "l_ 0 norm constraint lms algorithm for sparse system identification"
    - "a fast approach for overcomplete sparse decomposition based on smoothed ell 0 norm"

Connected through hyperedge HE-4 (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"
    - "a stochastic gradient approach on compressive sensing signal reconstruction based on adaptive filtering framework"
    - "l_ 0 norm constraint lms algorithm for sparse system identification"

Connected through hyperedge HE-5 (induced by source paper "improved adaptive sparse channel estimation using mixed square fourth error criterion"):
  with members:
    - "least mean square fourth algorithm with application to sparse channel estimation"

Connected through hyperedge HE-6 (induced by source paper "reweighted l1 norm penalized lms for sparse channel estimation and its analysis"):
  with members:
    - "sparse distributed learning based on diffusion adaptation"
    - "a sparsity promoting adaptive algorithm for distributed learning"
    - "l_ 0 norm constraint lms algorithm for sparse system identification"
    - "kalman filtered compressed sensing"

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"
    - "a stochastic gradient approach on compressive sensing signal reconstruction based on adaptive filtering framework"
    - "l_ 0 norm constraint lms algorithm for sparse system identification"

Connected through hyperedge HE-8 (induced by source paper "sparse lms via online linearized bregman iteration"):
  with members:
    - "online sparse system identification and signal reconstruction using projections onto weighted ell_ 1 balls"
    - "l_ 0 norm constraint lms algorithm for sparse system identification"
    - "sparse online learning via truncated gradient"

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)