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: "smart sampling for lightweight verification of markov decision processes"
  Abstract: Markov decision processes (MDP) are useful to model optimisation problems in concurrent systems. To verify MDPs with efficient Monte Carlo techniques requires that their nondeterminism be resolved by a scheduler. Recent work has introduced the elements of lightweight techniques to sample directly from scheduler space, but finding optimal schedulers by simple sampling may be inefficient. Here we describe "smart" sampling algorithms that can make substantial improvements in performance.

Connected through hyperedge HE-1 (induced by source paper "pac statistical model checking for markov decision processes and stochastic games"):
  with members:
    - "certified reinforcement learning with logic guidance"
    - "omega regular objectives in model free reinforcement learning"
    - "faster statistical model checking for unbounded temporal properties"
    - "probably approximately correct mdp learning and control with temporal logic constraints"
    - "scalable verification of markov decision processes"

Connected through hyperedge HE-2 (induced by source paper "monte carlo tree search for verifying reachability in markov decision processes"):
  with members:
    - "value iteration for long run average reward in markov decision processes"
    - "efficient strategy iteration for mean payoff in markov decision processes"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "a storm is coming a modern probabilistic model checker"
    - "verification of markov decision processes using learning algorithms"

Connected through hyperedge HE-3 (induced by source paper "a hierarchy of scheduler classes for stochastic automata"):
  with members:
    - "optimal continuous time markov decisions"
    - "scalable verification of markov decision processes"
    - "late weak bisimulation for markov automata"
    - "fixed delay events in generalized semi markov processes revisited"

Connected through hyperedge HE-4 (induced by source paper "automated experiment design for data efficient verification of parametric markov decision processes"):
  with members:
    - "parameter synthesis for markov models faster than ever"
    - "implementation and comparison of solution methods for decision processes with non markovian rewards"

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.DS(Data Structures and Algorithms)