human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "shepherding hordes of markov chains":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "counterexample driven synthesis for probabilistic program sketches")
  Member "parameter independent strategies for pmdps via pomdps":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "scenario based verification of uncertain mdps")
  Member "sea param exploring schedulers in parametric mdps":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on the complexity of reachability in parametric markov decision processes")
  Member "multi objective robust strategy synthesis for interval markov decision processes":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "efficiency through uncertainty scalable formal synthesis for stochastic hybrid systems")
  Member "parametric markov chains pctl complexity and fraction free gaussian elimination":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on the complexity of reachability in parametric markov decision processes")
  Member "sequential convex programming for the efficient verification of parametric mdps":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "scenario based verification of uncertain mdps")
  Member "reachability in augmented interval markov chains":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "are parametric markov chains monotonic")
  Member "a storm is coming a modern probabilistic model checker":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "the probabilistic model checker storm")

Question: Please tell me which class the source paper 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)