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 "perfect information stochastic games with generalized mean payoff objectives"):
  Abstract: Graph games provide the foundation for modeling and synthesizing reactive processes. In the synthesis of stochastic reactive processes, the traditional model is perfect-information stochastic games, where some transitions of the game graph are controlled by two adversarial players, and the other transitions are executed probabilistically. We consider such games where the objective is the conjunction of several quantitative objectives (specified as mean-payoff conditions), which we refer to as generalized mean-payoff objectives. The basic decision problem asks for the existence of a finite-memory strategy for a player that ensures the generalized mean-payoff objective be satisfied with a desired probability against all strategies of the opponent. A special case of the decision problem is the almost-sure problem where the desired probability is 1. Previous results presented a semi-decision procedure for epsilon-approximations of the almost-sure problem. In this work, we show that both the almost-sure problem as well as the general basic decision problem are coNP-complete, significantly improving the previous results. Moreover, we show that in the case of 1-player stochastic games, randomized memoryless strategies are sufficient and the problem can be solved in polynomial time. In contrast, in two-player stochastic games, we show that even with randomized strategies exponential memory is required in general, and present a matching exponential upper bound. We also study the basic decision problem with infinite-memory strategies and present computational complexity results for the problem. Our results are relevant in the synthesis of stochastic reactive systems with multiple quantitative requirements.

Includes members:
  Member "unifying two views on multiple mean payoff objectives in markov decision processes":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multi weighted markov decision processes with reachability objectives")
  Member "multidimensional beyond worst case and almost sure problems for mean payoff objectives":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multi weighted markov decision processes with reachability objectives")
  Member "fixed dimensional energy games are in pseudo polynomial time":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "games where you can play optimally with finite memory")
  Member "two player perfect information shift invariant submixing stochastic games are half positional":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "games with delays a frankenstein approach")
  Member "markov decision processes with multiple long run averageobjectives":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deciding fast termination for probabilistic vass with nondeterminism")
  Member "a framework for automated competitive analysis of on line scheduling of firm deadline tasks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "the complexity of multi mean payoff and multi energy games")
  Member "synthesis from ltl specifications with mean payoff objectives":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "symbolic bdd and add algorithms for energy games")
  Member "the complexity of multi mean payoff and multi energy games":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "games where you can play optimally with finite memory")

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)