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: "multi objective model checking of markov decision processes"
  Abstract: We study and provide efficient algorithms for multi-objective model checking#N#problems for Markov Decision Processes (MDPs). Given an MDP, M, and given#N#multiple linear-time (\omega -regular or LTL) properties \varphi\_i, and#N#probabilities r\_i \epsilon [0,1], i=1,...,k, we ask whether there exists a#N#strategy \sigma for the controller such that, for all i, the probability that a#N#trajectory of M controlled by \sigma satisfies \varphi\_i is at least r\_i. We#N#provide an algorithm that decides whether there exists such a strategy and if#N#so produces it, and which runs in time polynomial in the size of the MDP. Such#N#a strategy may require the use of both randomization and memory. We also#N#consider more general multi-objective \omega -regular queries, which we#N#motivate with an application to assume-guarantee compositional reasoning for#N#probabilistic systems.#N#  Note that there can be trade-offs between different properties: satisfying#N#property \varphi\_1 with high probability may necessitate satisfying \varphi\_2#N#with low probability. Viewing this as a multi-objective optimization problem,#N#we want information about the "trade-off curve" or Pareto curve for maximizing#N#the probabilities of different properties. We show that one can compute an#N#approximate Pareto curve with respect to a set of \omega -regular properties in#N#time polynomial in the size of the MDP.#N#  Our quantitative upper bounds use LP methods. We also study qualitative#N#multi-objective model checking problems, and we show that these can be analysed#N#by purely graph-theoretic methods, even though the strategies may still require#N#both randomization and memory.

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"
    - "parameter synthesis for markov models"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "simple strategies in multi objective mdps technical report"):
  with members:
    - "multi weighted markov decision processes with reachability objectives"
    - "markov automata with multiple objectives"
    - "threshold constraints with guarantees for parity objectives in markov decision processes"
    - "a storm is coming a modern probabilistic model checker"
    - "multi objective approaches to markov decision processes with uncertain transition parameters"
    - "variations on the stochastic shortest path problem"
    - "mixed integer quadratic programming is in np"
    - "percentile queries in multi dimensional markov decision processes"

Connected through hyperedge HE-3 (induced by source paper "approximating values of generalized reachability stochastic games"):
  with members:
    - "efficient strategy iteration for mean payoff in markov decision processes"
    - "decidability results for multi objective stochastic games"
    - "perfect information stochastic games with generalized mean payoff objectives"
    - "multigain a controller synthesis tool for mdps with multiple mean payoff objectives"
    - "verification of markov decision processes using learning algorithms"
    - "percentile queries in multi dimensional markov decision processes"
    - "robust multidimensional mean payoff games are undecidable"
    - "trading performance for stability in markov decision processes"

Connected through hyperedge HE-4 (induced by source paper "farkas certificates and minimal witnesses for probabilistic reachability constraints"):
  with members:
    - "counterexample explanation by learning small strategies in markov decision processes"
    - "verification of markov decision processes using learning algorithms"
    - "cegar for qualitative analysis of probabilistic systems"

Connected through hyperedge HE-5 (induced by source paper "conditional value at risk for reachability and mean payoff in markov decision processes"):
  with members:
    - "quantile markov decision process"
    - "value iteration for long run average reward in markov decision processes"
    - "maximizing the conditional expected reward for reaching the goal"
    - "multidimensional beyond worst case and almost sure problems for mean payoff objectives"
    - "the odds of staying on budget"
    - "percentile queries in multi dimensional markov decision processes"
    - "trading performance for stability in markov decision processes"
    - "computing quantiles in markov reward models"

Connected through hyperedge HE-6 (induced by source paper "structured synthesis for probabilistic systems"):
  with members:
    - "shepherding hordes of markov chains"
    - "sequential convex programming for the efficient verification of parametric mdps"
    - "a storm is coming a modern probabilistic model checker"
    - "parameter synthesis for markov models faster than ever"
    - "high level counterexamples for probabilistic automata"
    - "pareto curves for probabilistic model checking"

Connected through hyperedge HE-7 (induced by source paper "markov automata with multiple objectives"):
  with members:
    - "a storm is coming a modern probabilistic model checker"
    - "safety constrained reinforcement learning for mdps"
    - "variations on the stochastic shortest path problem"
    - "analysis of timed and long run objectives for markov automata"
    - "markov decision processes with multiple long run averageobjectives"
    - "a survey of multi objective sequential decision making"
    - "trading performance for stability in markov decision processes"
    - "meet your expectations with guarantees beyond worst case synthesis in quantitative games"

Connected through hyperedge HE-8 (induced by source paper "sequential convex programming for the efficient verification of parametric mdps"):
  with members:
    - "parameter synthesis for markov models faster than ever"
    - "the probabilistic model checker storm extended abstract"
    - "smoothed model checking for uncertain continuous time markov chains"
    - "accelerating parametric probabilistic verification"
    - "pareto curves for probabilistic model checking"

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)