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 "scenario based verification of uncertain mdps"):
  Abstract: We consider Markov decision processes (MDPs) in which the transition probabilities and rewards belong to an uncertainty set parametrized by a collection of random variables. The probability distributions for these random parameters are unknown. The problem is to compute the probability to satisfy a temporal logic specification within any MDP that corresponds to a sample from these unknown distributions. In general, this problem is undecidable, and we resort to techniques from so-called scenario optimization. Based on a finite number of samples of the uncertain parameters, each of which induces an MDP, the proposed method estimates the probability of satisfying the specification by solving a finite-dimensional convex optimization problem. The number of samples required to obtain a high confidence on this estimate is independent from the number of states and the number of random parameters. Experiments on a large set of benchmarks show that a few thousand samples suffice to obtain high-quality confidence bounds with a high probability.

Includes members:
  Member "parameter synthesis for markov models":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the probabilistic model checker storm")
  Member "parameter independent strategies for pmdps via pomdps":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "parameter synthesis for markov models")
  Member "incremental verification of parametric and reconfigurable markov chains":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "symblicit exploration and elimination for probabilistic model checking")
  Member "sequential convex programming for the efficient verification of parametric mdps":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "parameter synthesis for markov models")
  Member "a storm is coming a modern probabilistic model checker":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "the probabilistic model checker storm")
  Member "multi objective approaches to markov decision processes with uncertain transition parameters":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "simple strategies in multi objective mdps technical report")
  Member "parameter synthesis for markov models faster than ever":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "the probabilistic model checker storm")
  Member "incremental pruning a simple fast exact method for partially observable markov decision processes":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "optimizing sequential medical treatments with auto encoding heuristic search in pomdps")

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)