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 "value iteration for long run average reward in markov decision processes"):
  Abstract: Markov decision processes (MDPs) are standard models for probabilistic systems with non-deterministic behaviours. Long-run average rewards provide a mathematically elegant formalism for expressing long term performance. Value iteration (VI) is one of the simplest and most efficient algorithmic approaches to MDPs with other properties, such as reachability objectives. Unfortunately, a naive extension of VI does not work for MDPs with long-run average rewards, as there is no known stopping criterion. In this work our contributions are threefold. (1) We refute a conjecture related to stopping criteria for MDPs with long-run average rewards. (2) We present two practical algorithms for MDPs with long-run average rewards based on VI. First, we show that a combination of applying VI locally for each maximal end-component (MEC) and VI for reachability objectives can provide approximation guarantees. Second, extending the above approach with a simulation-guided on-demand variant of VI, we present an anytime algorithm that is able to deal with very large models. (3) Finally, we present experimental results showing that our methods significantly outperform the standard approaches on several benchmarks.

Includes members:
  Member "multigain a controller synthesis tool for mdps with multiple mean payoff objectives":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "approximating values of generalized reachability stochastic games")
  Member "unifying two views on multiple mean payoff objectives in markov decision processes":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multi weighted markov decision processes with reachability objectives")
  Member "knowledge spaces and the completeness of learning strategies":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multi weighted markov decision processes with reachability objectives")
  Member "the complexity of ergodic mean payoff games":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "ergodic mean payoff games for the analysis of attacks in crypto currencies")
  Member "verification of markov decision processes using learning algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "tractable reinforcement learning of signal temporal logic objectives")
  Member "markov decision processes with multiple long run averageobjectives":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deciding fast termination for probabilistic vass with nondeterminism")
  Member "faster algorithms for markov decision processes with low treewidth":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "fully polynomial time parameterized computations for graphs and matrices of low treewidth")
  Member "trading performance for stability in markov decision processes":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "life is random time is not markov decision processes with window objectives")

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.SY(Systems and Control)