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: "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.

Connected through hyperedge HE-1 (induced by source paper "perfect information stochastic games with generalized mean payoff objectives"):
  with members:
    - "unifying two views on multiple mean payoff objectives in markov decision processes"
    - "multidimensional beyond worst case and almost sure problems for mean payoff objectives"
    - "fixed dimensional energy games are in pseudo polynomial time"
    - "two player perfect information shift invariant submixing stochastic games are half positional"
    - "markov decision processes with multiple long run averageobjectives"
    - "a framework for automated competitive analysis of on line scheduling of firm deadline tasks"
    - "the complexity of multi mean payoff and multi energy games"
    - "finite memory strategy synthesis for robust multidimensional mean payoff objectives"

Connected through hyperedge HE-2 (induced by source paper "symbolic bdd and add algorithms for energy games"):
  with members:
    - "synthesizing a lego forklift controller in gr 1 a case study"
    - "strategy synthesis for multi dimensional quantitative objectives"
    - "generalized mean payoff and energy games"
    - "better quality in synthesis through quantitative objectives"

Connected through hyperedge HE-3 (induced by source paper "average energy games"):
  with members:
    - "limit your consumption finding bounds in average energy games"
    - "minimizing running costs in consumption systems"
    - "first cycle games"
    - "reachability in two clock timed automata is pspace complete"
    - "looking at mean payoff and total payoff through windows"
    - "automated synthesis of reliable and efficient systems through game theory a case study"
    - "the complexity of multi mean payoff and multi energy games"
    - "strategy synthesis for multi dimensional quantitative objectives"

Connected through hyperedge HE-4 (induced by source paper "qualitative analysis of concurrent mean payoff games"):
  with members:
    - "the complexity of partial observation stochastic parity games with finite memory strategies"
    - "the value 1 problem under finite memory strategies for concurrent mean payoff games"
    - "what is decidable about partially observable markov decision processes with omega regular objectives"
    - "decidable problems for probabilistic automata on infinite words"
    - "partial observation stochastic games how to win when belief fails"
    - "qualitative reachability in stochastic bpa games"
    - "measuring and synthesizing systems in probabilistic environments"
    - "the complexity of solving reachability games using value and strategy iteration"

Connected through hyperedge HE-5 (induced by source paper "robust multidimensional mean payoff games are undecidable"):
  with members:
    - "efficient controller synthesis for consumption games with multiple resource types"
    - "the complexity of multi mean payoff and multi energy games"
    - "finite memory strategy synthesis for robust multidimensional mean payoff objectives"
    - "strategy synthesis for multi dimensional quantitative objectives"
    - "hyperplane separation technique for multidimensional mean payoff games"
    - "mean payoff pushdown games"
    - "the complexity of mean payoff automaton expression"
    - "mean payoff automaton expressions"

Connected through hyperedge HE-6 (induced by source paper "model checking quantitative alternating time temporal logic on one counter game models"):
  with members:
    - "how to be both rich and happy combining quantitative and qualitative strategic reasoning about multi player games extended abstract"
    - "reachability games on extended vector addition systems with states"
    - "energy parity games"

Connected through hyperedge HE-7 (induced by source paper "symblicit algorithms for optimal strategy synthesis in monotonic markov decision processes"):
  with members:
    - "symblicit algorithms for optimal strategy synthesis in monotonic markov decision processes extended version"

Connected through hyperedge HE-8 (induced by source paper "looking at mean payoff and total payoff through windows"):
  with members:
    - "polynomial time algorithms for energy games with special weight structures"
    - "infinite state games with finitary conditions"
    - "meet your expectations with guarantees beyond worst case synthesis in quantitative games"
    - "the complexity of multi mean payoff and multi energy games"
    - "strategy synthesis for multi dimensional quantitative objectives"
    - "an o n 2 time algorithm for alternating b uchi games"
    - "the surprizing complexity of generalized reachability games"
    - "generalized mean payoff and energy games"

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.LO(Logic in Computer Science)