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: "quantitative games with interval objectives"
  Abstract: Traditionally quantitative games such as mean-payoff games and discount sum games have two players -- one trying to maximize the payoff, the other trying to minimize it. The associated decision problem, "Can Eve (the maximizer) achieve, for example, a positive payoff?" can be thought of as one player trying to attain a payoff in the interval $(0,\infty)$. In this paper we consider the more general problem of determining if a player can attain a payoff in a finite union of arbitrary intervals for various payoff functions (liminf, mean-payoff, discount sum, total sum). In particular this includes the interesting exact-value problem, "Can Eve achieve a payoff of exactly (e.g.) 0?"

Connected through hyperedge HE-1 (induced by source paper "decidability results for multi objective stochastic games"):
  with members:
    - "perfect information stochastic games with generalized mean payoff objectives"
    - "robust multidimensional mean payoff games are undecidable"
    - "the complexity of multi mean payoff and multi energy games"
    - "strategy synthesis for multi dimensional quantitative objectives"
    - "multi objective model checking of markov decision processes"
    - "first order and temporal logics for nested words"

Connected through hyperedge HE-2 (induced by source paper "percentile queries in multi dimensional markov decision processes"):
  with members:
    - "unifying two views on multiple mean payoff objectives in markov decision processes"
    - "the complexity of the kth largest subset problem and related problems"
    - "exact and approximate determinization of discounted sum automata"
    - "variations on the stochastic shortest path problem"
    - "the odds of staying on budget"
    - "decidability problems for actor systems"
    - "markov decision processes with multiple long run averageobjectives"
    - "to reach or not to reach efficient algorithms for total payoff 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)