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: "trading performance for stability in markov decision processes"
  Abstract: We study the complexity of central controller synthesis problems for finite-state Markov decision processes, where the objective is to optimize both the expected mean-payoff performance of the system and its stability. #R##N#We argue that the basic theoretical notion of expressing the stability in terms of the variance of the mean-payoff (called global variance in our paper) is not always sufficient, since it ignores possible instabilities on respective runs. For this reason we propose alernative definitions of stability, which we call local and hybrid variance, and which express how rewards on each run deviate from the run's own mean-payoff and from the expected mean-payoff, respectively. #R##N#We show that a strategy ensuring both the expected mean-payoff and the variance below given bounds requires randomization and memory, under all the above semantics of variance. We then look at the problem of determining whether there is a such a strategy. For the global variance, we show that the problem is in PSPACE, and that the answer can be approximated in pseudo-polynomial time. For the hybrid variance, the analogous decision problem is in NP, and a polynomial-time approximating algorithm also exists. For local variance, we show that the decision problem is in NP. Since the overall performance can be traded for stability (and vice versa), we also present algorithms for approximating the associated Pareto curve in all the three cases. #R##N#Finally, we study a special case of the decision problems, where we require a given expected mean-payoff together with zero variance. Here we show that the problems can be all solved in polynomial time.

Connected through hyperedge HE-1 (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"
    - "value iteration for long run average reward in markov decision processes"
    - "threshold constraints with guarantees for parity objectives in markov decision processes"
    - "a storm is coming a modern probabilistic model checker"

Connected through hyperedge HE-2 (induced by source paper "approximating values of generalized reachability stochastic games"):
  with members:
    - "efficient strategy iteration for mean payoff in markov decision processes"
    - "decidability results for multi objective stochastic games"
    - "perfect information stochastic games with generalized mean payoff objectives"
    - "multigain a controller synthesis tool for mdps with multiple mean payoff objectives"
    - "verification of markov decision processes using learning algorithms"
    - "percentile queries in multi dimensional markov decision processes"
    - "robust multidimensional mean payoff games are undecidable"
    - "pareto curves for probabilistic model checking"

Connected through hyperedge HE-3 (induced by source paper "conditional value at risk for reachability and mean payoff in markov decision processes"):
  with members:
    - "quantile markov decision process"
    - "value iteration for long run average reward in markov decision processes"
    - "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"
    - "computing quantiles in markov reward models"
    - "meet your expectations with guarantees beyond worst case synthesis in quantitative games"

Connected through hyperedge HE-4 (induced by source paper "expected window mean payoff"):
  with members:
    - "stability in graphs and games"
    - "on the complexity of heterogeneous multidimensional quantitative games"
    - "looking at mean payoff through foggy windows"
    - "the complexity of the kth largest subset problem and related problems"
    - "looking at mean payoff and total payoff through windows"

Connected through hyperedge HE-5 (induced by source paper "markov automata with multiple objectives"):
  with members:
    - "a storm is coming a modern probabilistic model checker"
    - "safety constrained reinforcement learning for mdps"
    - "variations on the stochastic shortest path problem"
    - "analysis of timed and long run objectives for markov automata"
    - "markov decision processes with multiple long run averageobjectives"
    - "a survey of multi objective sequential decision making"
    - "meet your expectations with guarantees beyond worst case synthesis in quantitative games"
    - "pareto curves for probabilistic model checking"

Connected through hyperedge HE-6 (induced by source paper "value iteration for long run average reward in markov decision processes"):
  with members:
    - "multigain a controller synthesis tool for mdps with multiple mean payoff objectives"
    - "unifying two views on multiple mean payoff objectives in markov decision processes"
    - "knowledge spaces and the completeness of learning strategies"
    - "the complexity of ergodic mean payoff games"
    - "verification of markov decision processes using learning algorithms"
    - "markov decision processes with multiple long run averageobjectives"
    - "faster algorithms for markov decision processes with low treewidth"
    - "assume guarantee abstraction refinement for probabilistic systems"

Connected through hyperedge HE-7 (induced by source paper "non zero sum games for reactive synthesis"):
  with members:
    - "weak subgame perfect equilibria and their application to quantitative reachability"
    - "assume admissible synthesis"
    - "reactive synthesis without regret"
    - "multidimensional beyond worst case and almost sure problems for mean payoff objectives"
    - "variations on the stochastic shortest path problem"
    - "secure equilibria in weighted games"
    - "percentile queries in multi dimensional markov decision processes"
    - "expectations or guarantees i want it all a crossroad between games and mdps"

Connected through hyperedge HE-8 (induced by source paper "controller synthesis for mdps and frequency ltl setminus gu"):
  with members:
    - "on frequency ltl in probabilistic systems"
    - "percentile queries in multi dimensional markov decision processes"
    - "from ltl to deterministic automata a safraless compositional approach"
    - "markov decision processes with multiple long run averageobjectives"
    - "automata with generalized rabin pairs for probabilistic model checking and ltl synthesis"
    - "multi weighted automata and mso logic"
    - "deterministic automata for the f g fragment of ltl"
    - "a probabilistic temporal logic with frequency operators and its model checking"

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)