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: "model free linear quadratic control via reduction to expert prediction"
  Abstract: Model-free approaches for reinforcement learning (RL) and continuous control find policies based only on past states and rewards, without fitting a model of the system dynamics. They are appealing as they are general purpose and easy to implement; however, they also come with fewer theoretical guarantees than model-based RL. In this work, we present a new model-free algorithm for controlling linear quadratic (LQ) systems, and show that its regret scales as $O(T^{\xi+2/3})$ for any small $\xi>0$ if time horizon satisfies $T>C^{1/\xi}$ for a constant $C$. The algorithm is based on a reduction of control of Markov decision processes to an expert prediction problem. In practice, it corresponds to a variant of policy iteration with forced exploration, where the policy in each phase is greedy with respect to the average of all previous value functions. This is the first model-free algorithm for adaptive control of LQ systems that provably achieves sublinear regret and has a polynomial computation cost. Empirically, our algorithm dramatically outperforms standard policy iteration, but performs worse than a model-based approach.

Connected through hyperedge HE-1 (induced by source paper "regret bound of adaptive control in linear quadratic gaussian lqg systems"):
  with members:
    - "regret minimization in partially observable linear quadratic control"
    - "improper learning for non stochastic control"
    - "learning linear dynamical systems with semi parametric least squares"
    - "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"
    - "learning linear quadratic regulators efficiently with only t regret"
    - "non asymptotic identification of lti systems from a single trajectory"

Connected through hyperedge HE-2 (induced by source paper "logarithmic regret for learning linear quadratic regulators efficiently"):
  with members:
    - "online control with adversarial disturbances"
    - "logarithmic regret for online control"
    - "near optimal finite time identification of arbitrary linear dynamical systems"
    - "derivative free methods for policy optimization guarantees for linear quadratic systems"
    - "global convergence of policy gradient methods for the linear quadratic regulator"
    - "spectral filtering for general linear dynamical systems"
    - "online linear quadratic control"
    - "regret bounds for robust adaptive control of the linear quadratic regulator"

Connected through hyperedge HE-3 (induced by source paper "regret minimization in partially observable linear quadratic control"):
  with members:
    - "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"
    - "spectral filtering for general linear dynamical systems"
    - "regret bounds for robust adaptive control of the linear quadratic regulator"
    - "learning without mixing towards a sharp analysis of linear system identification"
    - "learning based control of unknown linear systems with thompson sampling"

Connected through hyperedge HE-4 (induced by source paper "provably efficient reinforcement learning with linear function approximation"):
  with members:
    - "sample optimal parametric q learning using linearly additive features"
    - "variance reduced q learning is minimax optimal"
    - "provably efficient q learning with function approximation via distribution shift error checking oracle"
    - "learning linear quadratic regulators efficiently with only t regret"
    - "reinforcement leaning in feature space matrix bandit kernels and regret bound"
    - "lipschitz bandit optimization with improved efficiency"
    - "regret bounds for robust adaptive control of the linear quadratic regulator"
    - "efficient exploration through bayesian deep q networks"

Connected through hyperedge HE-5 (induced by source paper "the nonstochastic control problem"):
  with members:
    - "learning linear dynamical systems with semi parametric least squares"
    - "introduction to online convex optimization"
    - "online control with adversarial disturbances"
    - "finite time system identification for partially observed lti systems of unknown order"
    - "learning linear quadratic regulators efficiently with only t regret"
    - "logarithmic regret for online control"
    - "non asymptotic identification of lti systems from a single trajectory"
    - "global convergence of policy gradient methods for the linear quadratic regulator"

Connected through hyperedge HE-6 (induced by source paper "finite time analysis of approximate policy iteration for the linear quadratic regulator"):
  with members:
    - "finite sample analysis for sarsa and q learning with linear function approximation"
    - "learning linear quadratic regulators efficiently with only t regret"
    - "a finite time analysis of temporal difference learning with linear function approximation"
    - "near optimal finite time identification of arbitrary linear dynamical systems"
    - "the gap between model based and model free methods on the linear quadratic regulator an asymptotic viewpoint"
    - "simple random search provides a competitive approach to reinforcement learning"
    - "derivative free methods for policy optimization guarantees for linear quadratic systems"
    - "global convergence of policy gradient methods for the linear quadratic regulator"

Connected through hyperedge HE-7 (induced by source paper "boosting for dynamical systems"):
  with members:
    - "introduction to online convex optimization"
    - "online control with adversarial disturbances"
    - "spectral filtering for general linear dynamical systems"
    - "online linear quadratic control"
    - "regret bounds for robust adaptive control of the linear quadratic regulator"
    - "global convergence of policy gradient methods for linearized control problems"
    - "learning linear dynamical systems via spectral filtering"
    - "gradient descent learns linear dynamical systems"

Connected through hyperedge HE-8 (induced by source paper "exploration enhanced politex"):
  with members:
    - "provably efficient maximum entropy exploration"
    - "deepmind control suite"
    - "large scale study of curiosity driven learning"
    - "distributed prioritized experience replay"
    - "is q learning provably efficient"
    - "multi step reinforcement learning a unifying algorithm"
    - "prioritized experience replay"
    - "scale free algorithms for online linear optimization"

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.LG(Machine Learning)