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 "verification of markov decision processes using learning algorithms"):
  Abstract: We present a general framework for applying machine-learning algorithms to the verification of Markov decision processes (MDPs). The primary goal of these techniques is to improve performance by avoiding an exhaustive exploration of the state space. Our framework focuses on probabilistic reachability, which is a core property for verification, and is illustrated through two distinct instantiations. The first assumes that full knowledge of the MDP is available, and performs a heuristic-driven partial exploration of the model, yielding precise lower and upper bounds on the required probability. The second tackles the case where we may only sample the MDP, and yields probabilistic guarantees, again in terms of both the lower and upper bounds, which provides efficient stopping criteria for the approximation. The latter is the first extension of statistical model-checking for unbounded properties in MDPs. In contrast with other related approaches, we do not restrict our attention to time-bounded (finite-horizon) or discounted properties, nor assume any particular properties of the MDP. We also show how our techniques extend to LTL objectives. We present experimental results showing the performance of our framework on several examples.

Includes members:
  Member "automata with generalized rabin pairs for probabilistic model checking and ltl synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "near linear time algorithms for streett objectives in graphs and mdps")
  Member "scalable verification of markov decision processes":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "certified reinforcement learning with logic guidance")
  Member "lightweight monte carlo algorithm for markov decision processes":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "data driven and model based verification a bayesian identification approach")
  Member "deterministic automata for the f g fragment of ltl":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "one theorem to rule them all a unified translation of ltl into ω automata")
  Member "uppaal smc statistical model checking for priced timed automata":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "quantitative aspects of programming languages and systems over the past 2 4 years and beyond")
  Member "cross entropy optimisation of importance sampling parameters for statistical model checking":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "smoothed model checking for uncertain continuous time markov chains")
  Member "an o n 2 time algorithm for alternating b uchi games":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "cegar for qualitative analysis of probabilistic systems")

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)