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: "learning and designing stochastic processes from logical constraints"
  Abstract: Stochastic processes offer a flexible mathematical formalism to model and#N#reason about systems. Most analysis tools, however, start from the premises#N#that models are fully specified, so that any parameters controlling the#N#system's dynamics must be known exactly. As this is seldom the case, many#N#methods have been devised over the last decade to infer (learn) such parameters#N#from observations of the state of the system. In this paper, we depart from#N#this approach by assuming that our observations are {\it qualitative}#N#properties encoded as satisfaction of linear temporal logic formulae, as#N#opposed to quantitative observations of the state of the system. An important#N#feature of this approach is that it unifies naturally the system identification#N#and the system design problems, where the properties, instead of observations,#N#represent requirements to be satisfied. We develop a principled statistical#N#estimation procedure based on maximising the likelihood of the system's#N#parameters, using recent ideas from statistical machine learning. We#N#demonstrate the efficacy and broad applicability of our method on a range of#N#simple but non-trivial examples, including rumour spreading in social networks#N#and hybrid models of gene regulation.

Connected through hyperedge HE-1 (induced by source paper "policy learning for time bounded reachability in continuous time markov decision processes via doubly stochastic gradient ascent"):
  with members:
    - "optimal continuous time markov decisions"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "characterization and complexity results on jumping finite automata"):
  with members:
    - "complexity of problems of commutative grammars"
    - "on basic properties of jumping finite automata"
    - "two results on discontinuous input processing"
    - "commutative languages and their composition by consensual methods"
    - "converting nondeterministic automata and context free grammars into parikh equivalent one way and two way deterministic automata"
    - "petri net reachability graphs decidability status of first order properties"
    - "parikh s theorem a simple and direct automaton construction"
    - "bounded parikh automata"

Connected through hyperedge HE-3 (induced by source paper "data driven and model based verification a bayesian identification approach"):
  with members:
    - "smoothed model checking for uncertain continuous time markov chains"
    - "lightweight monte carlo algorithm for markov decision processes"
    - "statistical model checking an overview"

Connected through hyperedge HE-4 (induced by source paper "smoothed model checking for uncertain continuous time markov chains"):
  with members:
    - "learning temporal logical properties discriminating ecg models of cardiac arrhytmias"
    - "expectation propagation for approximate bayesian inference"
    - "on the robustness of temporal properties for stochastic models"
    - "hybrid performance modelling of opportunistic networks"
    - "cross entropy optimisation of importance sampling parameters for statistical model checking"

Connected through hyperedge HE-5 (induced by source paper "on the robustness of temporal properties for stochastic models"):
  with members:
    - "a temporal logic approach to modular design of synthetic biological circuits"
    - "a subsystems approach for parameter estimation of ode models of hybrid systems"
    - "information theoretic regret bounds for gaussian process optimization in the bandit setting"

Connected through hyperedge HE-6 (induced by source paper "learning temporal logical properties discriminating ecg models of cardiac arrhytmias"):
  with members:
    - "information theoretic regret bounds for gaussian process optimization in the bandit setting"

Connected through hyperedge HE-7 (induced by source paper "synthesis of large dynamic concurrent programs from dynamic specifications"):
  with members:
    - "dynamic input output automata"
    - "abstract model repair"
    - "synthesis of a simple self stabilizing system"

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)