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: "l_ 0 norm constraint lms algorithm for sparse system identification"
  Abstract: In order to improve the performance of least mean square (LMS) based system identification of sparse systems, a new adaptive algorithm is proposed which utilizes the sparsity property of such systems. A general approximating approach on l 0 norm-a typical metric of system sparsity, is proposed and integrated into the cost function of the LMS algorithm. This integration is equivalent to add a zero attractor in the iterations, by which the convergence rate of small coefficients, that dominate the sparse system, can be effectively improved. Moreover, using partial updating method, the computational complexity is reduced. The simulations demonstrate that the proposed algorithm can effectively improve the performance of LMS-based identification algorithms on sparse system.

Connected through hyperedge HE-1 (induced by source paper "nonconvex regularization based sparse and low rank recovery in signal processing statistics and machine learning"):
  with members:
    - "nonconvex optimization meets low rank matrix factorization an overview"
    - "efficient outlier removal for large scale global structure from motion"
    - "nonconvex regularization based sparse recovery and demixing with application to color image inpainting"
    - "deterministic approximate methods for maximum consensus robust fitting"
    - "unified scalable equivalent formulations for schatten quasi norms"
    - "enhanced low rank matrix approximation"
    - "scalable algorithms for tractable schatten quasi norm minimization"
    - "matrix completion has no spurious local minimum"

Connected through hyperedge HE-2 (induced by source paper "online wideband spectrum sensing using sparsity"):
  with members:
    - "iterative hard thresholding for compressed sensing"

Connected through hyperedge HE-3 (induced by source paper "diffusion leaky lms algorithm analysis and implementation"):
  with members:
    - "diffusion lms strategies in sensor networks with noisy input data"
    - "diffusion adaptation over multi agent networks with wireless link impairments"
    - "a novel normalized sign algorithm for system identification under impulsive noise interference"
    - "compressive diffusion strategies over distributed networks for reduced communication load"
    - "distributed decision making over adaptive networks"
    - "estimation of space time varying parameters using a diffusion lms algorithm"
    - "analysis of a reduced communication diffusion lms algorithm"
    - "sparse distributed learning based on diffusion adaptation"

Connected through hyperedge HE-4 (induced by source paper "dynamic mode decomposition for compressive system identification"):
  with members:
    - "using dynamic mode decomposition for real time background foreground separation in video"
    - "identification of sparse linear operators"
    - "online sparse system identification and signal reconstruction using projections onto weighted ell_ 1 balls"

Connected through hyperedge HE-5 (induced by source paper "diffusion l0 norm constraint improved proportionate lms algorithm for sparse distributed estimation"):
  with members:
    - "sparse distributed learning based on diffusion adaptation"
    - "diffusion strategies outperform consensus strategies for distributed estimation over adaptive networks"

Connected through hyperedge HE-6 (induced by source paper "proximal multitask learning over networks with sparsity inducing coregularization"):
  with members:
    - "unsupervised diffusion based lms for node specific parameter estimation over wireless sensor networks"
    - "diffusion lms over multitask networks"
    - "distributed diffusion based lms for node specific adaptive parameter estimation"
    - "diffusion adaptation strategies for distributed estimation over gaussian markov random fields"
    - "multitask diffusion adaptation over asynchronous networks"
    - "estimation of space time varying parameters using a diffusion lms algorithm"
    - "diffusion lms for clustered multitask networks"
    - "multitask diffusion adaptation over networks"

Connected through hyperedge HE-7 (induced by source paper "proportionate adaptive filtering for block sparse system identification"):
  with members:
    - "an improved variable step size zero point attracting projection algorithm"
    - "block sparsity induced adaptive filter for multi clustering system identification"
    - "a new variable step size zero point attracting projection algorithm"

Connected through hyperedge HE-8 (induced by source paper "study of distributed spectrum estimation using alternating mixed discrete continuous adaptation"):
  with members:
    - "sparse distributed learning based on diffusion adaptation"
    - "a sparsity promoting adaptive algorithm for distributed learning"

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)