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: "distributed recursive least squares stability and performance analysis"
  Abstract: The recursive least-squares (RLS) algorithm has well-documented merits for reducing complexity and storage requirements, when it comes to online estimation of stationary signals as well as for tracking slowly-varying nonstationary processes. In this paper, a distributed recursive least-squares (D-RLS) algorithm is developed for cooperative estimation using ad hoc wireless sensor networks. Distributed iterations are obtained by minimizing a separable reformulation of the exponentially-weighted least-squares cost, using the alternating-minimization algorithm. Sensors carry out reduced-complexity tasks locally, and exchange messages with one-hop neighbors to consent on the network-wide estimates adaptively. A steady-state mean-square error (MSE) performance analysis of D-RLS is conducted, by studying a stochastically-driven `averaged' system that approximates the D-RLS dynamics asymptotically in time. For sensor observations that are linearly related to the time-invariant parameter vector sought, the simplifying independence setting assumptions facilitate deriving accurate closed-form expressions for the MSE steady-state values. The problems of mean- and MSE-sense stability of D-RLS are also investigated, and easily-checkable sufficient conditions are derived under which a steady-state is attained. Without resorting to diminishing step-sizes which compromise the tracking ability of D-RLS, stability ensures that per sensor estimates hover inside a ball of finite radius centered at the true parameter vector, with high-probability, even when inter-sensor communication links are noisy. Interestingly, computer simulations demonstrate that the theoretical findings are accurate also in the pragmatic settings whereby sensors acquire temporally-correlated data.

Connected through hyperedge HE-1 (induced by source paper "recursive prediction of graph signals with incoming nodes"):
  with members:
    - "predicting graph signals using kernel regression where the input signal is agnostic to a graph"
    - "learning graphs from data a signal representation perspective"
    - "a comprehensive survey on graph neural networks"
    - "greedy sampling of graph signals"
    - "supervised linear regression for graph learning from graph signals"
    - "stationary graph processes and spectral estimation"
    - "graph sampling for covariance estimation"
    - "signals on graphs uncertainty principle and sampling"

Connected through hyperedge HE-2 (induced by source paper "a distributed algorithm for solving linear algebraic equations over random networks"):
  with members:
    - "decentralized gradient algorithm for solution of a linear equation"
    - "a distributed algorithm for solving a linear algebraic equation"
    - "a distributed algorithm for solving positive definite linear equations over networks with membership dynamics"

Connected through hyperedge HE-3 (induced by source paper "a recursive estimation approach to distributed identification of large scale multi input single output fir systems"):
  with members:
    - "distributed system identification with admm"

Connected through hyperedge HE-4 (induced by source paper "cloud aided collaborative estimation by admm rls algorithms for connected vehicle prognostics"):
  with members:
    - "multi agent distributed optimization via inexact consensus admm"
    - "distributed constrained optimization by consensus based primal dual perturbation method"
    - "diffusion strategies outperform consensus strategies for distributed estimation over adaptive networks"

Connected through hyperedge HE-5 (induced by source paper "decentralized rls with data adaptive censoring for regressions over large scale networks"):
  with members:
    - "resource allocation in a network based cloud computing environment design challenges"

Connected through hyperedge HE-6 (induced by source paper "learning based distributed detection estimation in sensor networks with unknown sensor defects"):
  with members:
    - "switching to learn"
    - "distributed kalman filtering over massive data sets analysis through large deviations of random riccati equations"
    - "distributed detection over noisy networks large deviations analysis"
    - "convergence rate analysis of distributed gossip linear parameter estimation fundamental limits and tradeoffs"
    - "gossip algorithms for distributed signal processing"
    - "distributed parameter estimation in sensor networks nonlinear observation models and imperfect communication"
    - "geographic gossip efficient averaging for sensor networks"

Connected through hyperedge HE-7 (induced by source paper "stochastic subgradient algorithms for strongly convex optimization over distributed networks"):
  with members:
    - "diffusion lms over multitask networks"
    - "multitask diffusion adaptation over networks"
    - "distributed pareto optimization via diffusion strategies"
    - "a simpler approach to obtaining an o 1 t convergence rate for the projected stochastic subgradient method"
    - "diffusion strategies outperform consensus strategies for distributed estimation over adaptive networks"
    - "performance limits for distributed estimation over lms adaptive networks"
    - "making gradient descent optimal for strongly convex stochastic optimization"
    - "distributed autonomous online learning regrets and intrinsic privacy preserving properties"

Connected through hyperedge HE-8 (induced by source paper "decentralized sparsity regularized rank minimization algorithms and applications"):
  with members:
    - "dmfsgd a decentralized matrix factorization algorithm for network distance prediction"
    - "recovery of low rank plus compressed sparse matrices with application to unveiling traffic anomalies"
    - "dynamic anomalography tracking network anomalies via sparsity and low rank"
    - "a sparsity promoting adaptive algorithm for distributed learning"
    - "cooperative convex optimization in networked systems augmented lagrangian algorithms with directed gossip communication"
    - "stable principal component pursuit"
    - "matrix completion with noise"
    - "robust principal component analysis"

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.NI(Networking and Internet Architecture)