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 "synthesis from ltl specifications with mean payoff objectives"):
  Abstract: The classical LTL synthesis problem is purely qualitative: the given LTL specification is realized or not by a reactive system. LTL is not expressive enough to formalize the correctness of reactive systems with respect to some quantitative aspects. This paper extends the qualitative LTL synthesis setting to a quantitative setting. The alphabet of actions is extended with a weight function ranging over the rational numbers. The value of an infinite word is the mean-payoff of the weights of its letters. The synthesis problem then amounts to automatically construct (if possible) a reactive system whose executions all satisfy a given LTL formula and have mean-payoff values greater than or equal to some given threshold. The latter problem is called LTLMP synthesis and the LTLMP realizability problem asks to check whether such a system exists. We first show that LTLMP realizability is not more difficult than LTL realizability: it is 2ExpTime-Complete. This is done by reduction to two-player mean-payoff parity games. While infinite memory strategies are required to realize LTLMP specifications in general, we show that epsilon-optimality can be obtained with finite memory strategies, for any epsilon > 0. To obtain an efficient algorithm in practice, we define a Safraless procedure to decide whether there exists a finite-memory strategy that realizes a given specification for some given threshold. This procedure is based on a reduction to two-player energy safety games which are in turn reduced to safety games. Finally, we show that those safety games can be solved efficiently by exploiting the structure of their state spaces and by using antichains as a symbolic data-structure. All our results extend to multi-dimensional weights. We have implemented an antichain-based procedure and we report on some promising experimental results.

Includes members:
  Member "state of buchi complementation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "good for mdps automata for probabilistic analysis and reinforcement learning")
  Member "strategy synthesis for multi dimensional quantitative objectives":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "games where you can play optimally with finite memory")
  Member "measuring permissiveness in parity games mean payoff parity games revisited":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "quantitative reductions and vertex ranked infinite games")
  Member "generalized mean payoff and energy games":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deciding fast termination for probabilistic vass with nondeterminism")
  Member "energy parity games":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "games where you can play optimally with finite memory")
  Member "better quality in synthesis through quantitative objectives":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "imitation projected programmatic reinforcement learning")

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)