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: "scalable verification of markov decision processes"
  Abstract: Markov decision processes (MDP) are useful to model concurrent process optimisation problems, but verifying them with numerical methods is often intractable. Existing approximative approaches do not scale well and are limited to memoryless schedulers. Here we present the basis of scalable verification for MDPSs, using an O(1) memory representation of history-dependent schedulers. We thus facilitate scalable learning techniques and the use of massively parallel verification.

Connected through hyperedge HE-1 (induced by source paper "certified reinforcement learning with logic guidance"):
  with members:
    - "the probabilistic model checker storm"
    - "deepsynth program synthesis for automatic task segmentation in deep reinforcement learning"
    - "rethinking the discount factor in reinforcement learning a decision theoretic approach"
    - "verifiably safe off model reinforcement learning"
    - "logically constrained reinforcement learning"
    - "playing hard exploration games by watching youtube"
    - "from game theoretic multi agent log linear learning to reinforcement learning"
    - "observe and look further achieving consistent performance on atari"

Connected through hyperedge HE-2 (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"
    - "smart sampling for lightweight verification of markov decision processes"
    - "probably approximately correct mdp learning and control with temporal logic constraints"

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

Connected through hyperedge HE-4 (induced by source paper "smart sampling for lightweight verification of markov decision processes"):
  with members:
    - "verification of markov decision processes using learning algorithms"
    - "an expectation transformer approach to predicate abstraction and data independence for probabilistic programs"

Connected through hyperedge HE-5 (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"
    - "lightweight monte carlo algorithm for 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)