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: "weighing and integrating evidence for stochastic simulation in bayesian networks"
  Abstract: Stochastic simulation approaches perform probabilistic inference in Bayesian networks by estimating the probability of an event based on the frequency that the event occurs in a set of simulation trials. This paper describes the evidence weighting mechanism, for augmenting the logic sampling stochastic simulation algorithm [Henrion, 1986]. Evidence weighting modifies the logic sampling algorithm by weighting each simulation trial by the likelihood of a network's evidence given the sampled state node values for that trial. We also describe an enhancement to the basic algorithm which uses the evidential integration technique [Chin and Cooper, 1987]. A comparison of the basic evidence weighting mechanism with the Markov blanket algorithm [Pearl, 1987], the logic sampling algorithm, and the evidence integration algorithm is presented. The comparison is aided by analyzing the performance of the algorithms in a simple example network.

Connected through hyperedge HE-1 (induced by source paper "gaussian mixture reduction for time constrained approximate inference in hybrid bayesian networks"):
  with members:
    - "loopy belief propagation for approximate inference an empirical study"
    - "inference in hybrid bayesian networks using mixtures of gaussians"
    - "cutset sampling for bayesian networks"

Connected through hyperedge HE-2 (induced by source paper "constraint based inference in probabilistic logic programs"):
  with members:
    - "inference in probabilistic logic programs using lifted explanations"
    - "new liftable classes for first order probabilistic inference"
    - "lifted variable elimination for probabilistic logic programming"
    - "stochastic logic programs sampling inference and applications"
    - "simulation approaches to general probabilistic inference on belief networks"
    - "clp bn constraint logic programming for probabilistic knowledge"
    - "the pita system tabling and answer subsumption for reasoning under uncertainty"
    - "logical hidden markov models"

Connected through hyperedge HE-3 (induced by source paper "using deep neural network approximate bayesian network"):
  with members:
    - "neural end to end learning for computational argumentation mining"
    - "loopy belief propagation for approximate inference an empirical study"
    - "backward simulation in bayesian networks"
    - "ais bn an adaptive importance sampling algorithm for evidential reasoning in large bayesian networks"

Connected through hyperedge HE-4 (induced by source paper "survey on models and techniques for root cause analysis"):
  with members:
    - "learning possibilistic logic theories from default rules"
    - "learning tractable probabilistic models for fault localization"
    - "using abduction in markov logic networks for root cause analysis"
    - "approximations in bayesian belief universe for knowledge based systems"
    - "automated reasoning using possibilistic logic semantics belief revision and variable certainty weights"
    - "loopy belief propagation for approximate inference an empirical study"
    - "a differential approach to inference in bayesian networks"
    - "evaluating computational models of explanation using human judgments"

Connected through hyperedge HE-5 (induced by source paper "learning to generate posters of scientific papers by probabilistic graphical models"):
  with members:
    - "learning to generate posters of scientific papers"

Connected through hyperedge HE-6 (induced by source paper "structured arc reversal and simulation of dynamic probabilistic networks"):
  with members:
    - "sensor validation using dynamic belief networks"
    - "a computational scheme for reasoning in dynamic probabilistic networks"
    - "learning bayesian networks with local structure"
    - "advances in probabilistic reasoning"
    - "a graph theoretic analysis of information value"
    - "context specific independence in bayesian networks"

Connected through hyperedge HE-7 (induced by source paper "some experiments with real time decision algorithms"):
  with members:
    - "the use of conflicts in searching bayesian networks"
    - "bounded conditioning flexible inference for decisions under scarce resources"
    - "fast belief update using order of magnitude probabilities"
    - "state space abstraction for anytime evaluation of probabilistic networks"
    - "localized partial evaluation of belief networks"
    - "incremental probabilistic inference"

Connected through hyperedge HE-8 (induced by source paper "optimal monte carlo estimation of belief network inference"):
  with members:
    - "search based methods to bound diagnostic probabilities in very large belief nets"
    - "improved sampling for diagnostic reasoning in bayesian networks"
    - "backward simulation in bayesian networks"
    - "knowledge engineering for large belief networks"
    - "simulation approaches to general probabilistic inference on belief networks"

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.AI(Artificial Intelligence)