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: "diffusion lms strategies in sensor networks with noisy input data"
  Abstract: We investigate the performance of distributed least-mean square (LMS) algorithms for parameter estimation over sensor networks where the regression data of each node are corrupted by white measurement noise. Under this condition, we show that the estimates produced by distributed LMS algorithms will be biased if the regression noise is excluded from consideration. We propose a bias-elimination technique and develop a novel class of diffusion LMS algorithms that can mitigate the effect of regression noise and obtain an unbiased estimate of the unknown parameter vector over the network. In our development, we first assume that the variances of the regression noises are known a priori. Later, we relax this assumption by estimating these variances in real time. We analyze the stability and convergence of the proposed algorithms and derive closed-form expressions to characterize their mean-square error performance in transient and steady-state regimes. We further provide computer experiment results that illustrate the efficiency of the proposed algorithms and support the analytical findings.

Connected through hyperedge HE-1 (induced by source paper "distributed interference cancellation in multi agent scenarios"):
  with members:
    - "counterfactual multi agent policy gradients"
    - "a distributional perspective on reinforcement learning"
    - "stationary signal processing on graphs"
    - "multi agent actor critic for mixed cooperative competitive environments"
    - "discrete signal processing on graphs sampling theory"
    - "diffusion lms over multitask networks"
    - "diffusion strategies outperform consensus strategies for distributed estimation over adaptive networks"

Connected through hyperedge HE-2 (induced by source paper "maximum total correntropy diffusion adaptation over networks with noisy links"):
  with members:
    - "diffusion least mean p power algorithms for distributed estimation in alpha stable noise environments"
    - "diffusion adaptation over networks"

Connected through hyperedge HE-3 (induced by source paper "diffusion leaky lms algorithm analysis and implementation"):
  with members:
    - "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"
    - "l_ 0 norm constraint lms algorithm for sparse system identification"

Connected through hyperedge HE-4 (induced by source paper "diffusion adaptation framework for compressive sensing reconstruction"):
  with members:
    - "adaptive diffusion schemes for heterogeneous networks"
    - "sparse distributed learning based on diffusion adaptation"
    - "diffusion adaptation over networks"
    - "fast and efficient compressive sensing using structurally random matrices"
    - "iterative hard thresholding for compressed sensing"

Connected through hyperedge HE-5 (induced by source paper "diffusion adaptation over multi agent networks with wireless link impairments"):
  with members:
    - "estimation of space time varying parameters using a diffusion lms algorithm"
    - "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-6 (induced by source paper "a class of diffusion algorithms with logarithmic cost over adaptive sparse volterra network"):
  with members:
    - "distributed decision making over adaptive networks"
    - "a novel family of adaptive filtering algorithms based on the logarithmic cost"
    - "estimation of space time varying parameters using a diffusion lms algorithm"
    - "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)