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: "lightweight monte carlo algorithm for markov decision processes"
  Abstract: Markov decision processes are widely used to model optimisation problems and concurrent systems, but only relatively small models may be solved exactly, due to the typically intractable number of states of a system. By considering schedulers based on the states visited by simulations, algorithms exist to find approximate solutions, but the number of states visited also becomes rapidly intractable. #R##N#We present a lightweight Monte Carlo algorithm that may be used for statistical model checking Markov decision processes and other models that mix nondeterminism with probabilistic transitions. The algorithm uses an O(1) memory representation of general schedulers, based on pseudo-random number generators and hash functions, and may be efficiently parallelised. We provide confidence bounds and propose novel ways in which the algorithm may be profitably extended.

Connected through hyperedge HE-1 (induced by source paper "data driven and model based verification a bayesian identification approach"):
  with members:
    - "learning and designing stochastic processes from logical constraints"
    - "smoothed model checking for uncertain continuous time markov chains"
    - "statistical model checking an overview"

Connected through hyperedge HE-2 (induced by source paper "verification of markov decision processes using learning algorithms"):
  with members:
    - "automata with generalized rabin pairs for probabilistic model checking and ltl synthesis"
    - "scalable verification of markov decision processes"
    - "deterministic automata for the f g fragment of ltl"
    - "uppaal smc statistical model checking for priced timed automata"
    - "cross entropy optimisation of importance sampling parameters for statistical model checking"
    - "an o n 2 time algorithm for alternating b uchi games"

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)