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: "hugs combining exact inference and gibbs sampling in junction trees"
  Abstract: Dawid, Kjaerulff and Lauritzen (1994) provided a preliminary description of a hybrid between Monte-Carlo sampling methods and exact local computations in junction trees. Utilizing the strengths of both methods, such hybrid inference methods has the potential of expanding the class of problems which can be solved under bounded resources as well as solving problems which otherwise resist exact solutions. The paper provides a detailed description of a particular instance of such a hybrid scheme; namely, combination of exact inference and Gibbs sampling in discrete Bayesian networks. We argue that this combination calls for an extension of the usual message passing scheme of ordinary junction trees.

Connected through hyperedge HE-1 (induced by source paper "a general algorithm for approximate inference and its application to hybrid bayes nets"):
  with members:
    - "stochastic simulation algorithms for dynamic probabilistic networks"
    - "computing upper and lower bounds on likelihoods in intractable networks"
    - "simulation approaches to general probabilistic inference on belief networks"

Connected through hyperedge HE-2 (induced by source paper "and or importance sampling"):
  with members:
    - "an empirical study of w cutset sampling for bayesian networks"
    - "approximate inference algorithms for hybrid bayesian networks with discrete constraints"
    - "ais bn an adaptive importance sampling algorithm for evidential reasoning in large bayesian networks"

Connected through hyperedge HE-3 (induced by source paper "an empirical study of w cutset sampling for bayesian networks"):
  with members:
    - "knowledge engineering for large belief networks"
    - "rao blackwellised particle filtering for dynamic bayesian networks"
    - "randomized algorithms for the loop cutset problem"

Connected through hyperedge HE-4 (induced by source paper "cutset sampling for bayesian networks"):
  with members:
    - "loopy belief propagation for approximate inference an empirical study"
    - "weighing and integrating evidence for stochastic simulation in bayesian networks"
    - "knowledge engineering for large belief networks"
    - "empirical evaluation of approximation algorithms for probabilistic decoding"
    - "simulation approaches to general probabilistic inference on belief networks"
    - "rao blackwellised particle filtering for dynamic bayesian networks"
    - "global conditioning for probabilistic inference in belief networks"
    - "an importance sampling algorithm based on evidence pre propagation"

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)