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: "exact and approximate determinization of discounted sum automata"
  Abstract: A discounted-sum automaton (NDA) is a nondeterministic finite automaton with#N#edge weights, valuing a run by the discounted sum of visited edge weights. More#N#precisely, the weight in the i-th position of the run is divided by#N#$\lambda^i$, where the discount factor $\lambda$ is a fixed rational number#N#greater than 1. The value of a word is the minimal value of the automaton runs#N#on it. Discounted summation is a common and useful measuring scheme, especially#N#for infinite sequences, reflecting the assumption that earlier weights are more#N#important than later weights. Unfortunately, determinization of NDAs, which is#N#often essential in formal verification, is, in general, not possible. We#N#provide positive news, showing that every NDA with an integral discount factor#N#is determinizable. We complete the picture by proving that the integers#N#characterize exactly the discount factors that guarantee determinizability: for#N#every nonintegral rational discount factor $\lambda$, there is a#N#nondeterminizable $\lambda$-NDA. We also prove that the class of NDAs with#N#integral discount factors enjoys closure under the algebraic operations min,#N#max, addition, and subtraction, which is not the case for general NDAs nor for#N#deterministic NDAs. For general NDAs, we look into approximate determinization,#N#which is always possible as the influence of a word's suffix decays. We show#N#that the naive approach, of unfolding the automaton computations up to a#N#sufficient level, is doubly exponential in the discount factor. We provide an#N#alternative construction for approximate determinization, which is singly#N#exponential in the discount factor, in the precision, and in the number of#N#states. We also prove matching lower bounds, showing that the exponential#N#dependency on each of these three parameters cannot be avoided. All our results#N#hold equally for automata over finite words and for automata over infinite#N#words.

Connected through hyperedge HE-1 (induced by source paper "comparator automata in quantitative verification"):
  with members:
    - "expressiveness and closure properties for quantitative languages"
    - "energy parity games"

Connected through hyperedge HE-2 (induced by source paper "reactive synthesis without regret"):
  with members:
    - "correct by synthesis reinforcement learning with temporal logic constraints"
    - "first cycle games"
    - "doomsday equilibria for omega regular games"
    - "iterated regret minimization in game graphs"
    - "generalized mean payoff and energy games"

Connected through hyperedge HE-3 (induced by source paper "minimizing regret in discounted sum games"):
  with members:
    - "reactive synthesis without regret"
    - "iterated regret minimization in game graphs"

Connected through hyperedge HE-4 (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"
    - "variations on the stochastic shortest path problem"
    - "the odds of staying on budget"
    - "quantitative games with interval objectives"
    - "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.FL(Formal Languages and Automata Theory)