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: "estimation of space time varying parameters using a diffusion lms algorithm"
  Abstract: We study the problem of distributed adaptive estimation over networks where nodes cooperate to estimate physical parameters that can vary over both space and time domains. We use a set of basis functions to characterize the space-varying nature of the parameters and propose a diffusion least mean-squares (LMS) strategy to recover these parameters from successive time measurements. We analyze the stability and convergence of the proposed algorithm, and derive closed-form expressions to predict its learning behavior and steady-state performance in terms of mean-square error. We find that in the estimation of the space-varying parameters using distributed approaches, the covariance matrix of the regression data at each node becomes rank-deficient. Our analysis reveals that the proposed algorithm can overcome this difficulty to a large extent by benefiting from the network stochastic matrices that are used to combine exchanged information between nodes. We provide computer experiments to illustrate and support the theoretical findings.

Connected through hyperedge HE-1 (induced by source paper "adaptation and learning over networks under subspace constraints part i stability analysis"):
  with members:
    - "adaptation and learning over networks under subspace constraints part ii performance analysis"
    - "efficient sampling set selection for bandlimited graph signals using graph spectral proxies"
    - "diffusion lms for multitask problems with local linear equality constraints"
    - "network lasso clustering and optimization in large graphs"
    - "distributed diffusion based lms for node specific adaptive parameter estimation"
    - "multitask diffusion adaptation over networks"
    - "asynchronous adaptation and learning over networks part i modeling and stability analysis"
    - "gossip algorithms for distributed signal processing"

Connected through hyperedge HE-2 (induced by source paper "multitask diffusion adaptation over networks with common latent representations"):
  with members:
    - "proximal multitask learning over networks with sparsity inducing coregularization"
    - "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"
    - "distributed diffusion based lms for node specific parameter estimation over adaptive networks"
    - "multitask diffusion adaptation over networks"
    - "distributed pareto optimization via diffusion strategies"
    - "diffusion adaptation over networks"

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"
    - "analysis of a reduced communication diffusion lms algorithm"
    - "sparse distributed learning based on diffusion adaptation"
    - "l_ 0 norm constraint lms algorithm for sparse system identification"

Connected through hyperedge HE-4 (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"
    - "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"
    - "on partial sparse recovery"

Connected through hyperedge HE-5 (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"
    - "diffusion lms for clustered multitask networks"
    - "multitask diffusion adaptation over networks"
    - "distributed pareto optimization via diffusion strategies"

Connected through hyperedge HE-6 (induced by source paper "diffusion lms for multitask problems with local linear equality constraints"):
  with members:
    - "proximal multitask learning over networks with sparsity inducing coregularization"
    - "dictionary learning over distributed models"
    - "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"
    - "multitask diffusion adaptation over asynchronous networks"
    - "on the mean square performance of the constrained lms algorithm"
    - "multitask diffusion adaptation over networks"

Connected through hyperedge HE-7 (induced by source paper "diffusion adaptation over multi agent networks with wireless link impairments"):
  with members:
    - "diffusion lms strategies in sensor networks with noisy input data"
    - "diffusion adaptation over networks"
    - "diffusion strategies outperform consensus strategies for distributed estimation over adaptive networks"
    - "convergence rate analysis of distributed gossip linear parameter estimation fundamental limits and tradeoffs"
    - "weight optimization for consensus algorithms with correlated switching topology"
    - "distributing the kalman filter for large scale systems"
    - "distributed consensus algorithms in sensor networks link failures and channel noise"
    - "distributed consensus algorithms in sensor networks quantized data and random link failures"

Connected through hyperedge HE-8 (induced by source paper "a class of diffusion algorithms with logarithmic cost over adaptive sparse volterra network"):
  with members:
    - "diffusion lms strategies in sensor networks with noisy input data"
    - "distributed decision making over adaptive networks"
    - "a novel family of adaptive filtering algorithms based on the logarithmic cost"
    - "diffusion least mean p power algorithms for distributed estimation in alpha stable noise environments"
    - "performance limits for distributed estimation over lms adaptive networks"

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.SY(Systems and Control)