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: "linear quadratic mean field teams optimal and approximately optimal decentralized solutions"
  Abstract: We consider team optimal control of decentralized systems with linear dynamics, quadratic costs, and arbitrary disturbance that consist of multiple sub-populations with exchangeable agents (i.e., exchanging two agents within the same sub-population does not affect the dynamics or the cost). Such a system is equivalent to one where the dynamics and costs are coupled across agents through the mean-field (or empirical mean) of the states and actions (even when the primitive random variables are non-exchangeable). Two information structures are investigated. In the first, all agents observe their local state and the mean-field of all sub-populations, in the second, all agents observe their local state but the mean-field of only a subset of the sub-populations. Both information structures are non-classical and not partially nested. Nonetheless, it is shown that linear control strategies are optimal for the first and approximately optimal for the second, the approximation error is inversely proportional to the size of the sub-populations whose mean-fields are not observed. The corresponding gains are determined by the solution of K+1 decoupled standard Riccati equations, where K is the number of sub-populations. The dimensions of the Riccati equations do not depend on the size of the sub-populations, thus the solution complexity is independent of the number of agents. Generalizations to major-minor agents, tracking cost, weighted mean-field, and infinite horizon are provided. The results are illustrated using an example of demand response in smart grids.

Connected through hyperedge HE-1 (induced by source paper "natural actor critic converges globally for hierarchical linear quadratic regulator"):
  with members:
    - "deep reinforcement learning for event driven multi agent decision processes"
    - "on the global convergence of actor critic a case for linear quadratic regulator with ergodic cost"
    - "the gap between model based and model free methods on the linear quadratic regulator an asymptotic viewpoint"
    - "derivative free methods for policy optimization guarantees for linear quadratic systems"
    - "global convergence of policy gradient methods for the linear quadratic regulator"
    - "mean field multi agent reinforcement learning"
    - "regret bounds for robust adaptive control of the linear quadratic regulator"
    - "learning without mixing towards a sharp analysis of linear system identification"

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.SY(Systems and Control)