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 "qualitative analysis of concurrent mean payoff games"):
  Abstract: We consider concurrent games played by two-players on a finite-state graph, where in every round the players simultaneously choose a move, and the current state along with the joint moves determine the successor state. We study a fundamental objective, namely, mean-payoff objective, where a reward is associated to each transition, and the goal of player 1 is to maximize the long-run average of the rewards, and the objective of player 2 is strictly the opposite. The path constraint for player 1 could be qualitative, i.e., the mean-payoff is the maximal reward, or arbitrarily close to it; or quantitative, i.e., a given threshold between the minimal and maximal reward. We consider the computation of the almost-sure (resp. positive) winning sets, where player 1 can ensure that the path constraint is satisfied with probability 1 (resp. positive probability). Our main results for qualitative path constraints are as follows: (1) we establish qualitative determinacy results that show that for every state either player 1 has a strategy to ensure almost-sure (resp. positive) winning against all player-2 strategies, or player 2 has a spoiling strategy to falsify almost-sure (resp. positive) winning against all player-1 strategies; (2) we present optimal strategy complexity results that precisely characterize the classes of strategies required for almost-sure and positive winning for both players; and (3) we present quadratic time algorithms to compute the almost-sure and the positive winning sets, matching the best known bound of algorithms for much simpler problems (such as reachability objectives). For quantitative constraints we show that a polynomial time solution for the almost-sure or the positive winning set would imply a solution to a long-standing open problem (the value problem for turn-based deterministic mean-payoff games) that is not known to be solvable in polynomial time.

Includes members:
  Member "the complexity of partial observation stochastic parity games with finite memory strategies":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "secure control under partial observability with temporal logic constraints")
  Member "the value 1 problem under finite memory strategies for concurrent mean payoff games":
    also connected through other hyperedges:
      (none)
  Member "what is decidable about partially observable markov decision processes with omega regular objectives":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "point based methods for model checking in partially observable markov decision processes")
  Member "synthesis from ltl specifications with mean payoff objectives":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "perfect information stochastic games with generalized mean payoff objectives")
  Member "decidable problems for probabilistic automata on infinite words":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the complexity of pomdps with long run average objectives")
  Member "partial observation stochastic games how to win when belief fails":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "alternating tree automata with qualitative semantics")
  Member "qualitative reachability in stochastic bpa games":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "reachability for branching concurrent stochastic games")
  Member "measuring and synthesizing systems in probabilistic environments":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "strategy representation by decision trees with linear classifiers")

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.GT(Computer Science and Game Theory)