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 "conditional value at risk for reachability and mean payoff in markov decision processes"):
  Abstract: We present the conditional value-at-risk (CVaR) in the context of Markov chains and Markov decision processes with reachability and mean-payoff objectives. CVaR quantifies risk by means of the expectation of the worst p-quantile. As such it can be used to design risk-averse systems. We consider not only CVaR constraints, but also introduce their conjunction with expectation constraints and quantile constraints (value-at-risk, VaR). We derive lower and upper bounds on the computational complexity of the respective decision problems and characterize the structure of the strategies in terms of memory and randomization.

Includes members:
  Member "quantile markov decision process":
    also connected through other hyperedges:
      (none)
  Member "value iteration for long run average reward in markov decision processes":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the probabilistic model checker storm")
  Member "maximizing the conditional expected reward for reaching the goal":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the probabilistic model checker storm")
  Member "multidimensional beyond worst case and almost sure problems for mean payoff objectives":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multi weighted markov decision processes with reachability objectives")
  Member "the odds of staying on budget":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "life is random time is not markov decision processes with window objectives")
  Member "percentile queries in multi dimensional markov decision processes":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "life is random time is not markov decision processes with window objectives")
  Member "trading performance for stability in markov decision processes":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "life is random time is not markov decision processes with window objectives")
  Member "computing quantiles in markov reward models":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "stochastic shortest paths and weight bounded properties in markov decision processes")

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)