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 adversarial online control"):
  Abstract: We introduce a new algorithm for online linear-quadratic control in a known system subject to adversarial disturbances. Existing regret bounds for this setting scale as $\sqrt{T}$ unless strong stochastic assumptions are imposed on the disturbance process. We give the first algorithm with logarithmic regret for arbitrary adversarial disturbance sequences, provided the state and control costs are given by known quadratic functions. Our algorithm and analysis use a characterization for the optimal offline control law to reduce the online control problem to (delayed) online learning with approximate advantage functions. Compared to previous techniques, our approach does not need to control movement costs for the iterates, leading to logarithmic regret.

Includes members:
  Member "logarithmic regret for learning linear quadratic regulators efficiently":
    also connected through other hyperedges:
      (none)
  Member "improper learning for non stochastic control":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "regret bound of adaptive control in linear quadratic gaussian lqg systems")
  Member "naive exploration is optimal for online lqr":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "improper learning for non stochastic control")
  Member "online convex optimization in adversarial markov decision processes":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning zero sum simultaneous move markov games using function approximation and correlated equilibrium")
  Member "introduction to online convex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (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-5 (induced by source paper "improper learning for non stochastic control")
  Member "logarithmic regret for online control":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "improper learning for non stochastic control")
  Member "the nonstochastic control problem":
    also connected through other hyperedges:
      - Hyperedge HE-7 (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)