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: "accelerated model checking of parametric markov chains"
  Abstract: Parametric Markov chains occur quite naturally in various applications: they can be used for a conservative analysis of probabilistic systems (no matter how the parameter is chosen, the system works to specification); they can be used to find optimal settings for a parameter; they can be used to visualise the influence of system parameters; and they can be used to make it easy to adjust the analysis for the case that parameters change. Unfortunately, these advancements come at a cost: parametric model checking is---or rather was---often slow. To make the analysis of parametric Markov models scale, we need three ingredients: clever algorithms, the right data structure, and good engineering. Clever algorithms are often the main (or sole) selling point; and we face the trouble that this paper focuses on -- the latter ingredients to efficient model checking. Consequently, our easiest claim to fame is in the speed-up we have often realised when comparing to the state of the art.

Connected through hyperedge HE-1 (induced by source paper "symblicit exploration and elimination for probabilistic model checking"):
  with members:
    - "optimistic value iteration"
    - "incremental verification of parametric and reconfigurable markov chains"
    - "sound value iteration"
    - "continuous time markov decisions based on partial exploration"
    - "a storm is coming a modern probabilistic model checker"
    - "explicit model checking of very large mdp using partitioning and secondary storage"
    - "verification of markov decision processes using learning algorithms"

Connected through hyperedge HE-2 (induced by source paper "on the complexity of reachability in parametric markov decision processes"):
  with members:
    - "parameter synthesis for markov models"
    - "qualitative reachability for open interval markov chains"
    - "sea param exploring schedulers in parametric mdps"
    - "parametric markov chains pctl complexity and fraction free gaussian elimination"
    - "a storm is coming a modern probabilistic model checker"
    - "parameter synthesis for markov models faster than ever"
    - "a symbolic sat based algorithm for almost sure reachability with small strategies in pomdps"
    - "accelerating parametric probabilistic verification"

Connected through hyperedge HE-3 (induced by source paper "are parametric markov chains monotonic"):
  with members:
    - "on the complexity of reachability in parametric markov decision processes"
    - "parametric markov chains pctl complexity and fraction free gaussian elimination"
    - "reachability in augmented interval markov chains"
    - "a storm is coming a modern probabilistic model checker"
    - "the complexity of graph based reductions for reachability in markov decision processes"
    - "parameter synthesis for markov models faster than ever"
    - "accelerating parametric probabilistic verification"

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)