human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "learning without mixing towards a sharp analysis of linear system identification"):
  Abstract: We prove that the ordinary least-squares (OLS) estimator attains nearly minimax optimal performance for the identification of linear dynamical systems from a single observed trajectory. Our upper bound relies on a generalization of Mendelson's small-ball method to dependent data, eschewing the use of standard mixing-time arguments. Our lower bounds reveal that these upper bounds match up to logarithmic factors. In particular, we capture the correct signal-to-noise behavior of the problem, showing that more unstable linear systems are easier to estimate. This behavior is qualitatively different from arguments which rely on mixing-time calculations that suggest that unstable systems are more difficult to estimate. We generalize our technique to provide bounds for a more general class of linear response time-series.

Includes members:
  Member "spectral filtering for general linear dynamical systems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "regret bound of adaptive control in linear quadratic gaussian lqg systems")
  Member "finite time analysis of optimal adaptive policies for linear quadratic systems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "regret bounds for decentralized learning in cooperative multi agent dynamical systems")
  Member "finite time identification in unstable linear systems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "non asymptotic and accurate learning of nonlinear dynamical systems")
  Member "learning linear dynamical systems via spectral filtering":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "regret bound of adaptive control in linear quadratic gaussian lqg systems")
  Member "gradient descent learns linear dynamical systems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "non asymptotic and accurate learning of nonlinear dynamical systems")
  Member "learning without concentration":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning with correntropy induced losses for regression with mixture of symmetric stable noise")

Question: Please tell me which class the source paper 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.LG(Machine Learning)