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: "the complexity of mean payoff automaton expression"
  Abstract: Quantitative languages are extension of boolean languages that assign to each word a real number. Mean-payoff automata are finite automata with numerical weights on transitions that assign to each infinite path the long-run average of the transition weights. The class of \emph{mean-payoff automaton expressions}, introduced in [1], is a class of quantitative languages, which is robust: it is closed under the four pointwise operations of max, min, sum and numerical complement.[1] In this paper we improve the computational complexity for solving the classical decision problems for mean-payoff automaton expressions: while the previously best known upper bound was 4EXPTIME, and no lower bound was known, we give an optimal PSPACE complete bound. As a consequence we also obtain a conceptually simple algorithm to solve the classical decision problems for mean-payoff automaton expressions.

Connected through hyperedge HE-1 (induced by source paper "decidable weighted expressions with presburger combinators"):
  with members:
    - "comparison of max plus automata and joint spectral radius of tropical matrices"
    - "quantitative languages defined by functional automata"
    - "nested weighted automata"
    - "regular combinators for string transformations"
    - "decision problems for additive regular functions"
    - "mean payoff automaton expressions"
    - "deciding unambiguity and sequentiality from a finitely ambiguous max plus automaton"

Connected through hyperedge HE-2 (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"
    - "mean payoff pushdown games"
    - "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-3 (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"
    - "mean payoff pushdown games"
    - "mean payoff automaton expressions"

Connected through hyperedge HE-4 (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"
    - "mean payoff pushdown games"
    - "qualitative reachability in stochastic bpa games"
    - "generalized mean payoff and energy games"

Connected through hyperedge HE-5 (induced by source paper "finite memory strategy synthesis for robust multidimensional mean payoff objectives"):
  with members:
    - "efficient controller synthesis for consumption games with multiple resource types"
    - "strategy synthesis for multi dimensional quantitative objectives"
    - "generalized mean payoff and energy games"
    - "expressiveness and closure properties for quantitative languages"
    - "mean payoff automaton expressions"

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)