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 "life is random time is not markov decision processes with window objectives"):
  Abstract: The window mechanism was introduced by Chatterjee et al. [1] to strengthen classical game objectives with time bounds. It permits to synthesize system controllers that exhibit acceptable behaviors within a configurable time frame, all along their infinite execution, in contrast to the traditional objectives that only require correctness of behaviors in the limit. The window concept has proved its interest in a variety of two-player zero-sum games, thanks to the ability to reason about such time bounds in system specifications, but also the increased tractability that it usually yields. #R##N#In this work, we extend the window framework to stochastic environments by considering the fundamental threshold probability problem in Markov decision processes for window objectives. That is, given such an objective, we want to synthesize strategies that guarantee satisfying runs with a given probability. We solve this problem for the usual variants of window objectives, where either the time frame is set as a parameter, or we ask if such a time frame exists. We develop a generic approach for window-based objectives and instantiate it for the classical mean-payoff and parity objectives, already considered in games. Our work paves the way to a wide use of the window mechanism in stochastic models. #R##N#[1] Krishnendu Chatterjee, Laurent Doyen, Mickael Randour, and Jean-Francois Raskin. Looking at mean-payoff and total-payoff through windows. Inf. Comput., 242:25-52, 2015.

Includes members:
  Member "multi weighted markov decision processes with reachability objectives":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "simple strategies in multi objective mdps technical report")
  Member "expected window mean payoff":
    also connected through other hyperedges:
      (none)
  Member "universal trees grow inside separating automata quasi polynomial lower bounds for parity games":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the strahler number of a parity game")
  Member "the complexity of mean payoff games using universal graphs":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "improving the complexity of parys recursive algorithm")
  Member "extending finite memory determinacy by boolean combination of winning conditions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "games where you can play optimally with finite memory")
  Member "value iteration for long run average reward in markov decision processes":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "the probabilistic model checker storm")
  Member "threshold constraints with guarantees for parity objectives in markov decision processes":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "simple strategies in multi objective mdps technical report")
  Member "a storm is coming a modern probabilistic model checker":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "the probabilistic model checker storm")

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.LO(Logic in Computer Science)