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 online control"):
  Abstract: We study optimal regret bounds for control in linear dynamical systems under adversarially changing strongly convex cost functions, given the knowledge of transition dynamics. This includes several well studied and fundamental frameworks such as the Kalman filter and the linear quadratic regulator. State of the art methods achieve regret which scales as $O(\sqrt{T})$, where $T$ is the time horizon. #R##N#We show that the optimal regret in this setting can be significantly smaller, scaling as $O(\text{poly}(\log T))$. This regret bound is achieved by two different efficient iterative methods, online gradient descent and online natural gradient.

Includes members:
  Member "introduction to online convex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "beyond ucb optimal and efficient contextual bandits with regression oracles")
  Member "online control with adversarial disturbances":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "improper learning for non stochastic control")
  Member "global convergence of policy gradient methods for the linear quadratic regulator":
    also connected through other hyperedges:
      - Hyperedge HE-3 (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-4 (induced by source paper "regret bound of adaptive control in linear quadratic gaussian lqg systems")
  Member "learning one convolutional layer with overlapping patches":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a deep conditioning treatment of neural networks")
  Member "online linear quadratic control":
    also connected through other hyperedges:
      - Hyperedge HE-6 (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-7 (induced by source paper "regret bound of adaptive control in linear quadratic gaussian lqg systems")
  Member "regret bounds for robust adaptive control of the linear quadratic regulator":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "improper learning for non stochastic control")

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)