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: "online linear quadratic control"
  Abstract: We study the problem of controlling linear time-invariant systems with known noisy dynamics and adversarially chosen quadratic losses. We present the first efficient online learning algorithms in this setting that guarantee $O(\sqrt{T})$ regret under mild assumptions, where $T$ is the time horizon. Our algorithms rely on a novel SDP relaxation for the steady-state distribution of the system. Crucially, and in contrast to previously proposed relaxations, the feasible solutions of our SDP all correspond to "strongly stable" policies that mix exponentially fast to a steady state.

Connected through hyperedge HE-1 (induced by source paper "improper learning for non stochastic control"):
  with members:
    - "naive exploration is optimal for online lqr"
    - "learning linear dynamical systems with semi parametric least squares"
    - "introduction to online convex optimization"
    - "finite sample analysis of stochastic system identification"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "beyond no regret competitive control via online optimization with memory"):
  with members:
    - "chasing convex bodies optimally"
    - "robust regression for safe exploration in control"
    - "episodic learning with control lyapunov functions for uncertain robotic systems"
    - "competitive online optimization under inventory constraints"
    - "online control with adversarial disturbances"
    - "beyond online balanced descent an optimal algorithm for smoothed online optimization"
    - "neural swarm decentralized close proximity multirotor controlusing learned interactions"
    - "online inventory management with application to energy procurement in data centers"

Connected through hyperedge HE-3 (induced by source paper "logarithmic regret for adversarial online control"):
  with members:
    - "logarithmic regret for learning linear quadratic regulators efficiently"
    - "improper learning for non stochastic control"
    - "naive exploration is optimal for online lqr"
    - "online convex optimization in adversarial markov decision processes"
    - "introduction to online convex optimization"
    - "online control with adversarial disturbances"
    - "logarithmic regret for online control"
    - "the nonstochastic control problem"

Connected through hyperedge HE-4 (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"
    - "model free linear quadratic control via reduction to expert prediction"
    - "regret bounds for robust adaptive control of the linear quadratic regulator"

Connected through hyperedge HE-5 (induced by source paper "online data poisoning attack"):
  with members:
    - "optimal adversarial attack on autoregressive models"
    - "online control with adversarial disturbances"
    - "training set camouflage"
    - "an optimal control approach to sequential machine teaching"
    - "an optimal control view of adversarial machine learning"
    - "data poisoning attacks in contextual bandits"
    - "data poisoning attacks against online learning"
    - "towards poisoning of deep learning algorithms with back gradient optimization"

Connected through hyperedge HE-6 (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-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"
    - "model free linear quadratic control via reduction to expert prediction"
    - "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 "logarithmic regret for online control"):
  with members:
    - "introduction to online convex optimization"
    - "online control with adversarial disturbances"
    - "global convergence of policy gradient methods for the linear quadratic regulator"
    - "spectral filtering for general linear dynamical systems"
    - "learning one convolutional layer with overlapping patches"
    - "model free linear quadratic control via reduction to expert prediction"
    - "regret bounds for robust adaptive control of the linear quadratic regulator"
    - "learning linear dynamical systems via spectral filtering"

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)