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: "finite time identification in unstable linear systems"
  Abstract: Identification of the parameters of stable linear dynamical systems is a well-studied problem in the literature, both in the low and high-dimensional settings. However, there are hardly any results for the unstable case, especially regarding finite time bounds. For this setting, classical results on least-squares estimation of the dynamics parameters are not applicable and therefore new concepts and technical approaches need to be developed to address the issue. Unstable linear systems reflect key real applications in control theory, econometrics, and finance. #R##N#This study establishes finite time bounds for the identification error of the least-squares estimates for a fairly large class of heavy-tailed noise distributions, and transition matrices of such systems. The results relate the time length required as a function of the problem dimension and key characteristics of the true underlying transition matrix and the noise distribution. To obtain them, appropriate concentration inequalities for random matrices and for sequences of martingale differences are leveraged.

Connected through hyperedge HE-1 (induced by source paper "non asymptotic and accurate learning of nonlinear dynamical systems"):
  with members:
    - "finite time analysis of approximate policy iteration for the linear quadratic regulator"
    - "learning linear quadratic regulators efficiently with only t regret"
    - "non asymptotic identification of lti systems from a single trajectory"
    - "near optimal finite time identification of arbitrary linear dynamical systems"
    - "derivative free methods for policy optimization guarantees for linear quadratic systems"
    - "stochastic gradient descent learns state equations with nonlinear activations"
    - "learning compact neural networks with regularization"
    - "global convergence of policy gradient methods for the linear quadratic regulator"

Connected through hyperedge HE-2 (induced by source paper "online learning of the kalman filter with logarithmic regret"):
  with members:
    - "naive exploration is optimal for online lqr"
    - "learning linear dynamical systems with semi parametric least squares"
    - "finite sample analysis of stochastic system identification"
    - "finite time system identification for partially observed lti systems of unknown order"
    - "learning linear quadratic regulators efficiently with only t regret"
    - "non asymptotic identification of lti systems from a single trajectory"
    - "near optimal finite time identification of arbitrary linear dynamical systems"
    - "spectral filtering for general linear dynamical systems"

Connected through hyperedge HE-3 (induced by source paper "no regret prediction in marginally stable systems"):
  with members:
    - "learning linear dynamical systems with semi parametric least squares"
    - "introduction to online convex optimization"
    - "robust guarantees for learning an autoregressive filter"
    - "finite sample analysis of stochastic system identification"
    - "finite time system identification for partially observed lti systems of unknown order"
    - "non asymptotic identification of lti systems from a single trajectory"
    - "near optimal finite time identification of arbitrary linear dynamical systems"
    - "spectral filtering for general linear dynamical systems"

Connected through hyperedge HE-4 (induced by source paper "active learning for identification of linear dynamical systems"):
  with members:
    - "learning linear dynamical systems with semi parametric least squares"
    - "sample complexity lower bounds for linear system identification"
    - "finite sample analysis of stochastic system identification"
    - "finite time system identification for partially observed lti systems of unknown order"
    - "learning linear quadratic regulators efficiently with only t regret"
    - "non asymptotic identification of lti systems from a single trajectory"
    - "near optimal finite time identification of arbitrary linear dynamical systems"
    - "spectral filtering for general linear dynamical systems"

Connected through hyperedge HE-5 (induced by source paper "learning linear dynamical systems with semi parametric least squares"):
  with members:
    - "non asymptotic identification of lti systems from a single trajectory"
    - "near optimal finite time identification of arbitrary linear dynamical systems"
    - "stochastic gradient descent learns state equations with nonlinear activations"
    - "spectral filtering for general linear dynamical systems"
    - "learning without mixing towards a sharp analysis of linear system identification"
    - "variance reduction methods for sublinear reinforcement learning"
    - "rebar low variance unbiased gradient estimates for discrete latent variable models"
    - "learning linear dynamical systems via spectral filtering"

Connected through hyperedge HE-6 (induced by source paper "on applications of bootstrap in continuous space reinforcement learning"):
  with members:
    - "near optimal finite time identification of arbitrary linear dynamical systems"
    - "simple random search provides a competitive approach to reinforcement learning"
    - "derivative free methods for policy optimization guarantees for linear quadratic systems"
    - "on optimality of adaptive linear quadratic regulators"
    - "garbage in reward out bootstrapping exploration in multi armed bandits"
    - "new insights into bootstrapping for bandits"
    - "finite time analysis of optimal adaptive policies for linear quadratic systems"
    - "regret analysis for adaptive linear quadratic policies"

Connected through hyperedge HE-7 (induced by source paper "from parameter estimation to dispersion of nonstationary gauss markov processes"):
  with members:
    - "the dispersion of the gauss markov source"
    - "non asymptotic identification of lti systems from a single trajectory"
    - "near optimal finite time identification of arbitrary linear dynamical systems"
    - "learning without mixing towards a sharp analysis of linear system identification"
    - "fixed length lossy compression in the finite blocklength regime"
    - "the dispersion of lossy source coding"
    - "a note on rate distortion functions for nonstationary gaussian autoregressive processes"

Connected through hyperedge HE-8 (induced by source paper "finite time system identification for partially observed lti systems of unknown order"):
  with members:
    - "learning linear dynamical systems with semi parametric least squares"
    - "non asymptotic identification of lti systems from a single trajectory"
    - "spectral filtering for general linear dynamical systems"
    - "learning without mixing towards a sharp analysis of linear system identification"
    - "lazysvd even faster svd decomposition yet without agonizing pain"
    - "gradient descent learns linear dynamical systems"

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)