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 over multitask networks"
  Abstract: The diffusion LMS algorithm has been extensively studied in recent years. This efficient strategy allows to address distributed optimization problems over networks in the case where nodes have to collaboratively estimate a single parameter vector. Nevertheless, there are several problems in practice that are multitask-oriented in the sense that the optimum parameter vector may not be the same for every node. This brings up the issue of studying the performance of the diffusion LMS algorithm when it is run, either intentionally or unintentionally, in a multitask environment. In this paper, we conduct a theoretical analysis on the stochastic behavior of diffusion LMS in the case where the single-task hypothesis is violated. We analyze the competing factors that influence the performance of diffusion LMS in the multitask environment, and which allow the algorithm to continue to deliver performance superior to non-cooperative strategies in some useful circumstances. We also propose an unsupervised clustering strategy that allows each node to select, via adaptive adjustments of combination weights, the neighboring nodes with which it can collaborate to estimate a common parameter vector. Simulations are presented to illustrate the theoretical results, and to demonstrate the efficiency of the proposed clustering strategy.

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"
    - "diffusion lms strategies in sensor networks with noisy input data"
    - "discrete signal processing on graphs sampling theory"
    - "diffusion strategies outperform consensus strategies for distributed estimation over adaptive networks"

Connected through hyperedge HE-2 (induced by source paper "adaptation and learning over networks under subspace constraints part ii performance analysis"):
  with members:
    - "adaptation and learning over networks under subspace constraints part i stability analysis"
    - "proximal multitask learning over networks with sparsity inducing coregularization"
    - "distributed pareto optimization via diffusion strategies"
    - "asynchronous adaptation and learning over networks part i modeling and stability analysis"

Connected through hyperedge HE-3 (induced by source paper "learning over multitask graphs part ii performance analysis"):
  with members:
    - "learning over multitask graphs part i stability analysis"
    - "diffusion lms for multitask problems with local linear equality constraints"
    - "proximal multitask learning over networks with sparsity inducing coregularization"
    - "network lasso clustering and optimization in large graphs"
    - "learning laplacian matrix in smooth graph signal representations"
    - "distributed diffusion based lms for node specific adaptive parameter estimation"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"
    - "multitask diffusion adaptation over networks"

Connected through hyperedge HE-4 (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"
    - "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"
    - "diffusion lms for clustered multitask networks"

Connected through hyperedge HE-5 (induced by source paper "multitask diffusion adaptation over networks with common latent representations"):
  with members:
    - "proximal multitask learning over networks with sparsity inducing coregularization"
    - "unsupervised diffusion based lms for node specific parameter estimation over wireless sensor networks"
    - "distributed diffusion based lms for node specific adaptive parameter estimation"
    - "estimation of space time varying parameters using a diffusion lms algorithm"
    - "distributed diffusion based lms for node specific parameter estimation over adaptive networks"
    - "multitask diffusion adaptation over networks"
    - "distributed pareto optimization via diffusion strategies"
    - "diffusion adaptation over networks"

Connected through hyperedge HE-6 (induced by source paper "distributed learning for stochastic generalized nash equilibrium problems"):
  with members:
    - "multitask diffusion adaptation over asynchronous networks"
    - "multitask diffusion adaptation over networks"
    - "on the learning behavior of adaptive networks part ii performance analysis"
    - "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"
    - "diffusion strategies outperform consensus strategies for distributed estimation over adaptive networks"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"

Connected through hyperedge HE-7 (induced by source paper "adaptive diffusion schemes for heterogeneous networks"):
  with members:
    - "compressive diffusion strategies over distributed networks for reduced communication load"
    - "distributed diffusion based lms for node specific adaptive parameter estimation"
    - "diffusion adaptation strategies for distributed estimation over gaussian markov random fields"
    - "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-8 (induced by source paper "distributed adaptive learning of graph signals"):
  with members:
    - "multitask diffusion adaptation over networks with common latent representations"
    - "reconstruction of graph signals through percolation from seeding nodes"
    - "adaptive least mean squares estimation of graph signals"
    - "sampling of graph signals with successive local aggregations"
    - "signals on graphs uncertainty principle and sampling"
    - "subsampling for graph power spectrum estimation"
    - "local set based graph signal reconstruction"
    - "discrete signal processing on graphs sampling theory"

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)