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: "cross entropy optimisation of importance sampling parameters for statistical model checking"
  Abstract: Statistical model checking avoids the exponential growth of states associated with probabilistic model checking by estimating properties from multiple executions of a system and by giving results within confidence bounds. Rare properties are often very important but pose a particular challenge for simulation-based approaches, hence a key objective under these circumstances is to reduce the number and length of simulations necessary to produce a given level of confidence. Importance sampling is a well-established technique that achieves this, however to maintain the advantages of statistical model checking it is necessary to find good importance sampling distributions without considering the entire state space. #R##N#Motivated by the above, we present a simple algorithm that uses the notion of cross-entropy to find the optimal parameters for an importance sampling distribution. In contrast to previous work, our algorithm uses a low dimensional vector of parameters to define this distribution and thus avoids the often intractable explicit representation of a transition matrix. We show that our parametrisation leads to a unique optimum and can produce many orders of magnitude improvement in simulation efficiency. We demonstrate the efficacy of our methodology by applying it to models from reliability engineering and biochemistry.

Connected through hyperedge HE-1 (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"
    - "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"
    - "an o n 2 time algorithm for alternating b uchi games"

Connected through hyperedge HE-2 (induced by source paper "smoothed model checking for uncertain continuous time markov chains"):
  with members:
    - "learning and designing stochastic processes from logical constraints"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "uppaal smc statistical model checking for priced timed automata"):
  with members:
    - "computing nash equilibrium in wireless ad hoc networks a simulation based approach"
    - "proceedings 10th international workshop on parallel and distributed methods in verification"
    - "distributed parametric and statistical model checking"
    - "on zone based analysis of duration probabilistic automata"

Connected through hyperedge HE-4 (induced by source paper "statistical model checking for stochastic hybrid systems"):
  with members:
    - "computing nash equilibrium in wireless ad hoc networks a simulation based approach"
    - "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.PF(Performance)