human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "distributed decision making over adaptive networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "diffusion multi rate lms algorithm for acoustic sensor networks")
  Member "sparse distributed learning based on diffusion adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deterministic and randomized diffusion based iterative generalized hard thresholding difight for distributed sparse signal recovery")
  Member "diffusion adaptation over networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "sparsity constrained distributed unmixing of hyperspectral data")
  Member "diffusion strategies outperform consensus strategies for distributed estimation over adaptive networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "decentralized multi agent reinforcement learning with networked agents recent advances")
  Member "performance limits for distributed estimation over lms adaptive networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "detection under one bit messaging over adaptive networks")
  Member "convergence rate analysis of distributed gossip linear parameter estimation fundamental limits and tradeoffs":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "supervised learning under distributed features")
  Member "gossip algorithms for distributed signal processing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "inverse graph learning over optimization networks")

Question: Please tell me which class the source paper 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)