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: "optimal continuous time markov decisions"
  Abstract: In the context of Markov decision processes running in continuous time, one of the most intriguing challenges is the efficient approximation of finite horizon reachability objectives. A multitude of sophisticated model checking algorithms have been proposed for this. However, no proper benchmarking has been performed thus far. #R##N#This paper presents a novel and yet simple solution: an algorithm originally developed for a restricted subclass of models and a subclass of schedulers can be twisted so as to become competitive with the more sophisticated algorithms in full generality. As the second main contribution, we perform a comparative evaluation of the core algorithmic concepts on an extensive set of benchmarks varying over all key parameters: model size, amount of non-determinism, time horizon, and precision.

Connected through hyperedge HE-1 (induced by source paper "continuous time markov decisions based on partial exploration"):
  with members:
    - "value iteration for long run average reward in markov decision processes"
    - "verification of markov decision processes using learning algorithms"

Connected through hyperedge HE-2 (induced by source paper "a hierarchy of scheduler classes for stochastic automata"):
  with members:
    - "smart sampling for lightweight verification of markov decision processes"
    - "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-3 (induced by source paper "policy learning for time bounded reachability in continuous time markov decision processes via doubly stochastic gradient ascent"):
  with members:
    - "learning and designing stochastic processes from logical constraints"
    - "smoothed model checking for uncertain continuous time markov chains"
    - "path integral policy improvement with covariance matrix adaptation"
    - "finite optimal control for time bounded reachability in ctmdps and continuous time markov games"
    - "experiments with infinite horizon policy gradient estimation"

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.SY(Systems and Control)