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: "lambda policy iteration a review and a new implementation"
  Abstract: In this paper we discuss $\l$-policy iteration, a method for exact and approximate dynamic programming. It is intermediate between the classical value iteration (VI) and policy iteration (PI) methods, and it is closely related to optimistic (also known as modified) PI, whereby each policy evaluation is done approximately, using a finite number of VI. We review the theory of the method and associated questions of bias and exploration arising in simulation-based cost function approximation. We then discuss various implementations, which offer advantages over well-established PI methods that use LSPE($\l$), LSTD($\l$), or TD($\l$) for policy evaluation with cost function approximation. One of these implementations is based on a new simulation scheme, called geometric sampling, which uses multiple short trajectories rather than a single infinitely long trajectory.

Connected through hyperedge HE-1 (induced by source paper "beyond the one step greedy approach in reinforcement learning"):
  with members:
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "asynchronous methods for deep reinforcement learning"
    - "high dimensional continuous control using generalized advantage estimation"
    - "trust region policy optimization"
    - "how to discount deep reinforcement learning towards new dynamic strategies"
    - "performance bounds for lambda policy iteration and application to the game of tetris"

Connected through hyperedge HE-2 (induced by source paper "convergence analysis of policy iteration"):
  with members:
    - "stabilizing value iteration with and without approximation errors"

Connected through hyperedge HE-3 (induced by source paper "optimal triggering of networked control systems"):
  with members:
    - "stabilizing value iteration with and without approximation errors"
    - "theoretical and numerical analysis of approximate dynamic programming with approximation errors"
    - "design of state based schedulers for a network of control loops"

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)