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: "parameter synthesis for markov models"
  Abstract: Markov chain analysis is a key technique in reliability engineering. A practical obstacle is that all probabilities in Markov models need to be known. However, system quantities such as failure rates or packet loss ratios, etc. are often not---or only partially---known. This motivates considering parametric models with transitions labeled with functions over parameters. Whereas traditional Markov chain analysis evaluates a reliability metric for a single, fixed set of probabilities, analysing parametric Markov models focuses on synthesising parameter values that establish a given reliability or performance specification $\varphi$. Examples are: what component failure rates ensure the probability of a system breakdown to be below 0.00000001?, or which failure rates maximise reliability? This paper presents various analysis algorithms for parametric Markov chains and Markov decision processes. We focus on three problems: (a) do all parameter values within a given region satisfy $\varphi$?, (b) which regions satisfy $\varphi$ and which ones do not?, and (c) an approximate version of (b) focusing on covering a large fraction of all possible parameter values. We give a detailed account of the various algorithms, present a software tool realising these techniques, and report on an extensive experimental evaluation on benchmarks that span a wide range of applications.

Connected through hyperedge HE-1 (induced by source paper "the probabilistic model checker storm"):
  with members:
    - "counterexample driven synthesis for probabilistic program sketches"
    - "safety analysis for vehicle guidance systems with dynamic fault trees"
    - "value iteration for long run average reward in markov decision processes"
    - "markov automata with multiple objectives"
    - "maximizing the conditional expected reward for reaching the goal"
    - "a storm is coming a modern probabilistic model checker"
    - "parameter synthesis for markov models faster than ever"
    - "high level counterexamples for probabilistic automata"

Connected through hyperedge HE-2 (induced by source paper "on the complexity of reachability in parametric markov decision processes"):
  with members:
    - "qualitative reachability for open interval markov chains"
    - "accelerated model checking of parametric 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 "scenario based verification of uncertain mdps"):
  with members:
    - "parameter independent strategies for pmdps via pomdps"
    - "incremental verification of parametric and reconfigurable markov chains"
    - "sequential convex programming for the efficient verification of parametric mdps"
    - "a storm is coming a modern probabilistic model checker"
    - "multi objective approaches to markov decision processes with uncertain transition parameters"
    - "parameter synthesis for markov models faster than ever"
    - "incremental pruning a simple fast exact method for partially observable markov decision processes"

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)