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 adaptation strategies for distributed estimation over gaussian markov random fields"
  Abstract: The aim of this paper is to propose diffusion strategies for distributed estimation over adaptive networks, assuming the presence of spatially correlated measurements distributed according to a Gaussian Markov random field (GMRF) model. The proposed methods incorporate prior information about the statistical dependency among observations, while at the same time processing data in real time and in a fully decentralized manner. A detailed mean-square analysis is carried out in order to prove stability and evaluate the steady-state performance of the proposed strategies. Finally, we also illustrate how the proposed techniques can be easily extended in order to incorporate thresholding operators for sparsity recovery applications. Numerical results show the potential advantages of using such techniques for distributed learning in adaptive networks deployed over GMRF.

Connected through hyperedge HE-1 (induced by source paper "adaptive diffusion schemes for heterogeneous networks"):
  with members:
    - "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"
    - "on the influence of informed agents on learning and adaptation over networks"
    - "sparse distributed learning based on diffusion adaptation"
    - "asynchronous adaptation and learning over networks part i modeling and stability analysis"
    - "on the learning behavior of adaptive networks part i transient analysis"
    - "diffusion adaptation over networks"

Connected through hyperedge HE-2 (induced by source paper "adaptive least mean squares estimation of graph signals"):
  with members:
    - "sampling of graph signals with successive local aggregations"
    - "signals on graphs uncertainty principle and sampling"
    - "toward an uncertainty principle for weighted graphs"
    - "local set based graph signal reconstruction"
    - "discrete signal processing on graphs sampling theory"
    - "on the degrees of freedom of signals on graphs"
    - "signal recovery on graphs fundamental limits of sampling strategies"
    - "a distributed tracking algorithm for reconstruction of graph signals"

Connected through hyperedge HE-3 (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"
    - "multitask diffusion adaptation over asynchronous networks"
    - "estimation of space time varying parameters using a diffusion lms algorithm"
    - "diffusion lms for clustered multitask networks"
    - "multitask diffusion adaptation over networks"
    - "distributed pareto optimization via diffusion strategies"

Connected through hyperedge HE-4 (induced by source paper "next in network nonconvex optimization"):
  with members:
    - "multi agent distributed optimization via inexact consensus admm"
    - "hybrid random deterministic parallel algorithms for nonconvex big data optimization"
    - "parallel selective algorithms for big data optimization"
    - "distributed detection and estimation in wireless sensor networks"
    - "distributed compressed sensing for static and time varying networks"
    - "decomposition by partial linearization parallel optimization of multi agent systems"
    - "distributed maximum likelihood sensor network localization"
    - "sparse distributed learning based on diffusion adaptation"

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)