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: "mean payoff pushdown games"
  Abstract: Two-player games on graphs is central in many problems in formal verification and program analysis such as synthesis and verification of open systems. In this work we consider solving recursive game graphs (or pushdown game graphs) that can model the control flow of sequential programs with recursion. While pushdown games have been studied before with qualitative objectives, such as reachability and $\omega$-regular objectives, in this work we study for the first time such games with the most well-studied quantitative objective, namely, mean-payoff objectives. In pushdown games two types of strategies are relevant: (1) global strategies, that depend on the entire global history; and (2) modular strategies, that have only local memory and thus does not depend on the context of invocation, but only on the history of the current invocation of the module. Our main results are as follows (1) One-player pushdown games with mean-payoff objectives under global strategies is decidable in polynomial time. (2) Two-player pushdown games with mean-payoff objectives under global strategies is undecidable. (3) One-player pushdown games with mean-payoff objectives under modular strategies is NP-hard. (4) Two-player pushdown games with mean-payoff objectives under modular strategies can be solved in NP (i.e., both one-player and two-player pushdown games with mean-payoff objectives under modular strategies is NP-complete). We also establish the optimal strategy complexity showing that global strategies for mean-payoff objectives require infinite memory even in one-player pushdown games; and memoryless modular strategies are sufficient in two-player pushdown games. Finally we also show that all the problems have the same complexity if the stack boundedness condition is added, where along with the mean-payoff objective the player must also ensure that the stack height is bounded.

Connected through hyperedge HE-1 (induced by source paper "bounding average energy games"):
  with members:
    - "undecidability of two dimensional robot games"
    - "limit your consumption finding bounds in average energy games"
    - "average energy games"
    - "fixed dimensional energy games are in pseudo polynomial time"
    - "reachability in succinct one counter 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"

Connected through hyperedge HE-2 (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"
    - "synthesis from ltl specifications with mean payoff objectives"
    - "the complexity of multi mean payoff and multi energy games"

Connected through hyperedge HE-3 (induced by source paper "faster algorithms for quantitative verification in constant treewidth graphs"):
  with members:
    - "nested weighted automata"
    - "faster algorithms for markov decision processes with low treewidth"
    - "the complexity of mean payoff automaton expression"
    - "measuring and synthesizing systems in probabilistic environments"
    - "expressiveness and closure properties for quantitative languages"
    - "mean payoff automaton expressions"
    - "better quality in synthesis through quantitative objectives"

Connected through hyperedge HE-4 (induced by source paper "nested weighted automata"):
  with members:
    - "quantitative languages defined by functional automata"
    - "weighted logics for nested words and algebraic formal power series"
    - "expressiveness and closure properties for quantitative languages"
    - "mean payoff automaton expressions"
    - "probabilistic weighted automata"

Connected through hyperedge HE-5 (induced by source paper "edit distance for pushdown automata"):
  with members:
    - "nested weighted automata"

Connected through hyperedge HE-6 (induced by source paper "robust multidimensional mean payoff games are undecidable"):
  with members:
    - "synthesis from ltl specifications with mean payoff objectives"
    - "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"
    - "the complexity of mean payoff automaton expression"
    - "mean payoff automaton expressions"

Connected through hyperedge HE-7 (induced by source paper "infinite state energy games"):
  with members:
    - "solving parity games on integer vectors"
    - "simulation over one counter nets is pspace complete"
    - "strategy synthesis for multi dimensional quantitative objectives"
    - "hyperplane separation technique for multidimensional mean payoff games"
    - "reachability games on extended vector addition systems with states"
    - "generalized mean payoff and energy games"
    - "energy parity games"

Connected through hyperedge HE-8 (induced by source paper "hyperplane separation technique for multidimensional mean payoff games"):
  with members:
    - "undecidability of two dimensional robot games"
    - "fixed dimensional energy games are in pseudo polynomial time"
    - "synthesis from ltl specifications with mean payoff objectives"
    - "efficient controller synthesis for consumption games with multiple resource types"
    - "the complexity of multi mean payoff and multi energy games"
    - "the complexity of mean payoff automaton expression"
    - "qualitative reachability in stochastic bpa 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)