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: "parametric strategy iteration"
  Abstract: Program behavior may depend on parameters, which are either configured before compilation time, or provided at run-time, e.g., by sensors or other input devices. Parametric program analysis explores how different parameter settings may affect the program behavior. #R##N#In order to infer invariants depending on parameters, we introduce parametric strategy iteration. This algorithm determines the precise least solution of systems of integer equations depending on surplus parameters. Conceptually, our algorithm performs ordinary strategy iteration on the given integer system for all possible parameter settings in parallel. This is made possible by means of region trees to represent the occurring piecewise affine functions. We indicate that each required operation on these trees is polynomial-time if only constantly many parameters are involved. #R##N#Parametric strategy iteration for systems of integer equations allows to construct parametric integer interval analysis as well as parametric analysis of differences of integer variables. It thus provides a general technique to realize precise parametric program analysis if numerical properties of integer variables are of concern.

Connected through hyperedge HE-1 (induced by source paper "efficient strategy iteration for mean payoff in markov decision processes"):
  with members:
    - "value iteration for long run average reward in markov decision processes"
    - "efficient parallel strategy improvement for parity games"
    - "synthesising strategy improvement and recursive algorithms for solving 2 5 player parity games"
    - "multigain a controller synthesis tool for mdps with multiple mean payoff objectives"
    - "verification of markov decision processes using learning algorithms"
    - "assume guarantee abstraction refinement for probabilistic systems"
    - "using strategy improvement to stay alive"
    - "the complexity of solving reachability games using value and strategy iteration"

Connected through hyperedge HE-2 (induced by source paper "difference constraints an adequate abstraction for complexity analysis of imperative programs"):
  with members:
    - "a simple and scalable static analysis for bound analysis and amortized complexity analysis"

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.PL(Programming Languages)