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: "smoothed model checking for uncertain continuous time markov chains"
  Abstract: We consider the problem of computing the satisfaction probability of a formula for stochastic models with parametric uncertainty. We show that this satisfaction probability is a smooth function of the model parameters. This enables us to devise a novel Bayesian statistical algorithm which performs statistical model checking simultaneously for all values of the model parameters from observations of truth values of the formula over individual runs of the model at isolated parameter values. This is achieved by exploiting the smoothness of the satisfaction function: by modelling explicitly correlations through a prior distribution over a space of smooth functions (a Gaussian Process), we can condition on observations at individual parameter values to construct an analytical approximation of the function itself. Extensive experiments on non-trivial case studies show that the approach is accurate and several orders of magnitude faster than naive parameter exploration with standard statistical model checking methods.

Connected through hyperedge HE-1 (induced by source paper "parameter synthesis for markov models"):
  with members:
    - "shepherding hordes of markov chains"
    - "parameter independent strategies for pmdps via pomdps"
    - "sea param exploring schedulers in parametric mdps"
    - "multi objective robust strategy synthesis for interval markov decision processes"
    - "parametric markov chains pctl complexity and fraction free gaussian elimination"
    - "sequential convex programming for the efficient verification of parametric mdps"
    - "reachability in augmented interval markov chains"
    - "a storm is coming a modern probabilistic model checker"

Connected through hyperedge HE-2 (induced by source paper "central limit model checking"):
  with members:
    - "probabilistic model checking for continuous time markov chains via sequential bayesian inference"
    - "programming discrete distributions with chemical reaction networks"
    - "efficient checking of individual rewards properties in markov population models"
    - "fluid model checking"
    - "hybrid behaviour of markov population models"

Connected through hyperedge HE-3 (induced by source paper "how to learn a model checker"):
  with members:
    - "deepxplore automated whitebox testing of deep learning systems"
    - "feature guided black box safety testing of deep neural networks"
    - "output reachable set estimation and verification for multi layer neural networks"
    - "deepsafe a data driven approach for checking adversarial robustness in neural networks"
    - "learning deep control policies for autonomous aerial vehicles with mpc guided policy search"
    - "on the robustness of temporal properties for stochastic models"

Connected through hyperedge HE-4 (induced by source paper "sequential convex programming for the efficient verification of parametric mdps"):
  with members:
    - "parameter synthesis for markov models faster than ever"
    - "the probabilistic model checker storm extended abstract"
    - "accelerating parametric probabilistic verification"
    - "pareto curves for probabilistic model checking"
    - "multi objective model checking of markov decision processes"

Connected through hyperedge HE-5 (induced by source paper "model checking markov population models by stochastic approximations"):
  with members:
    - "probabilistic model checking for continuous time markov chains via sequential bayesian inference"
    - "fluid model checking of timed properties"
    - "efficient checking of individual rewards properties in markov population models"
    - "fluid model checking"
    - "model checking of continuous time markov chains against timed automata specifications"

Connected through hyperedge HE-6 (induced by source paper "sea param exploring schedulers in parametric mdps"):
  with members:
    - "sequential convex programming for the efficient verification of parametric mdps"
    - "parameter synthesis for markov models faster than ever"
    - "accelerating parametric probabilistic verification"

Connected through hyperedge HE-7 (induced by source paper "probabilistic model checking for continuous time markov chains via sequential bayesian inference"):
  with members:
    - "model checking markov population models by stochastic approximations"
    - "fluid model checking"

Connected through hyperedge HE-8 (induced by source paper "bayesian verification under model uncertainty"):
  with members:
    - "statistical model checking an overview"

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.LO(Logic in Computer Science)