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: "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.

Connected through hyperedge HE-1 (induced by source paper "the probabilistic model checker storm"):
  with members:
    - "counterexample driven synthesis for probabilistic program sketches"
    - "safety analysis for vehicle guidance systems with dynamic fault trees"
    - "parameter synthesis for markov models"
    - "markov automata with multiple objectives"
    - "maximizing the conditional expected reward for reaching the goal"
    - "a storm is coming a modern probabilistic model checker"
    - "parameter synthesis for markov models faster than ever"
    - "high level counterexamples for probabilistic automata"

Connected through hyperedge HE-2 (induced by source paper "life is random time is not markov decision processes with window objectives"):
  with members:
    - "multi weighted markov decision processes with reachability objectives"
    - "expected window mean payoff"
    - "universal trees grow inside separating automata quasi polynomial lower bounds for parity games"
    - "the complexity of mean payoff games using universal graphs"
    - "extending finite memory determinacy by boolean combination of winning conditions"
    - "threshold constraints with guarantees for parity objectives in markov decision processes"
    - "a storm is coming a modern probabilistic model checker"
    - "window parity games an alternative approach toward parity games with time bounds"

Connected through hyperedge HE-3 (induced by source paper "logically constrained reinforcement learning"):
  with members:
    - "safe reinforcement learning via shielding"
    - "asynchronous methods for deep reinforcement learning"
    - "reinforcement learning with temporal logic rewards"
    - "correct by synthesis reinforcement learning with temporal logic constraints"
    - "safety constrained reinforcement learning for mdps"
    - "verification of markov decision processes using learning algorithms"
    - "a learning based approach to control synthesis of markov decision processes for linear temporal logic specifications"
    - "probably approximately correct mdp learning and control with temporal logic constraints"

Connected through hyperedge HE-4 (induced by source paper "conditional value at risk for reachability and mean payoff in markov decision processes"):
  with members:
    - "quantile markov decision process"
    - "maximizing the conditional expected reward for reaching the goal"
    - "multidimensional beyond worst case and almost sure problems for mean payoff objectives"
    - "the odds of staying on budget"
    - "percentile queries in multi dimensional markov decision processes"
    - "trading performance for stability in markov decision processes"
    - "computing quantiles in markov reward models"
    - "meet your expectations with guarantees beyond worst case synthesis in quantitative games"

Connected through hyperedge HE-5 (induced by source paper "monte carlo tree search for verifying reachability in markov decision processes"):
  with members:
    - "efficient strategy iteration for mean payoff in markov decision processes"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "a storm is coming a modern probabilistic model checker"
    - "smart sampling for lightweight verification of markov decision processes"
    - "verification of markov decision processes using learning algorithms"

Connected through hyperedge HE-6 (induced by source paper "continuous time markov decisions based on partial exploration"):
  with members:
    - "optimal continuous time markov decisions"
    - "verification of markov decision processes using learning algorithms"

Connected through hyperedge HE-7 (induced by source paper "value iteration for simple stochastic games stopping criterion and learning algorithm"):
  with members:
    - "a storm is coming a modern probabilistic model checker"

Connected through hyperedge HE-8 (induced by source paper "efficient strategy iteration for mean payoff in markov decision processes"):
  with members:
    - "efficient parallel strategy improvement for parity games"
    - "synthesising strategy improvement and recursive algorithms for solving 2 5 player parity games"
    - "multigain a controller synthesis tool for mdps with multiple mean payoff objectives"
    - "verification of markov decision processes using learning algorithms"
    - "parametric strategy iteration"
    - "assume guarantee abstraction refinement for probabilistic systems"
    - "using strategy improvement to stay alive"
    - "the complexity of solving reachability games using value and strategy iteration"

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)