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 "a multitask diffusion strategy with optimized inter cluster cooperation"):
  Abstract: We consider a multitask estimation problem where nodes in a network are divided into several connected clusters, with each cluster performing a least-mean-squares estimation of a different random parameter vector. Inspired by the adapt-then-combine diffusion strategy, we propose a multitask diffusion strategy whose mean stability can be ensured whenever individual nodes are stable in the mean, regardless of the inter-cluster cooperation weights. In addition, the proposed strategy is able to achieve an asymptotically unbiased estimation, when the parameters have same mean. We also develop an inter-cluster cooperation weights selection scheme that allows each node in the network to locally optimize its inter-cluster cooperation weights. Numerical results demonstrate that our approach leads to a lower average steady-state network mean-square deviation, compared with using weights selected by various other commonly adopted methods in the literature.

Includes members:
  Member "distributed local linear parameter estimation using gaussian spawn":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "compressive privacy for a linear dynamical system")
  Member "robust decentralized detection and social learning in tandem networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "an unsupervised bayesian neural network for truth discovery")
  Member "unsupervised diffusion based lms for node specific parameter estimation over wireless sensor networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multitask diffusion adaptation over networks with common latent representations")
  Member "diffusion lms over multitask networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "distributed interference cancellation in multi agent scenarios")
  Member "compressive diffusion strategies over distributed networks for reduced communication load":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "adaptive diffusion schemes for heterogeneous networks")
  Member "distributed diffusion based lms for node specific adaptive parameter estimation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "adaptation and learning over networks under subspace constraints part i stability analysis")
  Member "distributed localization of a rf target in nlos environments":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "towards information privacy for the internet of things")
  Member "multitask diffusion adaptation over asynchronous networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "distributed learning for stochastic generalized nash equilibrium problems")

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)