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 for clustered multitask networks"
  Abstract: Recent research works on distributed adaptive networks have intensively studied the case where the nodes estimate a common parameter vector collaboratively. However, there are many applications that are multitask-oriented in the sense that there are multiple parameter vectors that need to be inferred simultaneously. In this paper, we employ diffusion strategies to develop distributed algorithms that address clustered multitask problems by minimizing an appropriate mean-square error criterion with $\ell_2$-regularization. Some results on the mean-square stability and convergence of the algorithm are also provided. Simulations are conducted to illustrate the theoretical findings.

Connected through hyperedge HE-1 (induced by source paper "a multitask diffusion strategy with optimized inter cluster cooperation"):
  with members:
    - "distributed local linear parameter estimation using gaussian spawn"
    - "robust decentralized detection and social learning in tandem networks"
    - "unsupervised diffusion based lms for node specific parameter estimation over wireless sensor networks"
    - "diffusion lms over multitask networks"
    - "compressive diffusion strategies over distributed networks for reduced communication load"
    - "distributed diffusion based lms for node specific adaptive parameter estimation"
    - "distributed localization of a rf target in nlos environments"
    - "multitask diffusion adaptation over asynchronous networks"

Connected through hyperedge HE-2 (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"
    - "multitask diffusion adaptation over networks"
    - "distributed pareto optimization via diffusion strategies"

Connected through hyperedge HE-3 (induced by source paper "diffusion adaptation over clustered multitask networks based on the affine projection algorithm"):
  with members:
    - "diffusion lms over multitask networks"
    - "multitask diffusion adaptation over networks"
    - "diffusion strategies outperform consensus strategies for distributed estimation over adaptive networks"

Connected through hyperedge HE-4 (induced by source paper "distributed multi task apa over adaptive networks based on partial diffusion"):
  with members:
    - "diffusion lms over multitask networks"
    - "multitask diffusion adaptation over networks"
    - "diffusion strategies outperform consensus strategies for distributed estimation over adaptive networks"

Connected through hyperedge HE-5 (induced by source paper "multitask diffusion adaptation over networks"):
  with members:
    - "distributed decision making over adaptive networks"
    - "distributed adaptive networks a graphical evolutionary game theoretic view"
    - "distributed pareto optimization via diffusion strategies"
    - "sparse distributed learning based on diffusion adaptation"
    - "diffusion adaptation over networks"
    - "diffusion strategies outperform consensus strategies for distributed estimation over adaptive networks"
    - "a sparsity promoting adaptive algorithm for distributed learning"
    - "distributed consensus algorithms in sensor networks link failures and channel noise"

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)