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 "logarithmic regret for learning linear quadratic regulators efficiently"):
  Abstract: We consider the problem of learning in Linear Quadratic Control systems whose transition parameters are initially unknown. Recent results in this setting have demonstrated efficient learning algorithms with regret growing with the square root of the number of decision steps. We present new efficient algorithms that achieve, perhaps surprisingly, regret that scales only (poly)logarithmically with the number of steps in two scenarios: when only the state transition matrix $A$ is unknown, and when only the state-action transition matrix $B$ is unknown and the optimal policy satisfies a certain non-degeneracy condition. On the other hand, we give a lower bound that shows that when the latter condition is violated, square root regret is unavoidable.

Includes members:
  Member "online control with adversarial disturbances":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "improper learning for non stochastic control")
  Member "logarithmic regret for online control":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "improper learning for non stochastic control")
  Member "near optimal finite time identification of arbitrary linear dynamical systems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "non asymptotic and accurate learning of nonlinear dynamical systems")
  Member "derivative free methods for policy optimization guarantees for linear quadratic systems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "non asymptotic convergence of adam type reinforcement learning algorithms under markovian sampling")
  Member "global convergence of policy gradient methods for the linear quadratic regulator":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "accelerating reinforcement learning with a directional gaussian smoothing evolution strategy")
  Member "spectral filtering for general linear dynamical systems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "regret bound of adaptive control in linear quadratic gaussian lqg systems")
  Member "online linear quadratic control":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "improper learning for non stochastic control")
  Member "model free linear quadratic control via reduction to expert prediction":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "regret bound of adaptive control in linear quadratic gaussian lqg systems")

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)