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: "global conditioning for probabilistic inference in belief networks"
  Abstract: In this paper we propose a new approach to probabilistic inference on belief networks, global conditioning, which is a simple generalization of Pearl's (1986b) method of loopcutset conditioning. We show that global conditioning, as well as loop-cutset conditioning, can be thought of as a special case of the method of Lauritzen and Spiegelhalter (1988) as refined by Jensen et al (199Oa; 1990b). Nonetheless, this approach provides new opportunities for parallel processing and, in the case of sequential processing, a tradeoff of time for memory. We also show how a hybrid method (Suermondt and others 1990) combining loop-cutset conditioning with Jensen's method can be viewed within our framework. By exploring the relationships between these methods, we develop a unifying framework in which the advantages of each approach can be combined successfully.

Connected through hyperedge HE-1 (induced by source paper "query dags a practical paradigm for implementing belief network inference"):
  with members:
    - "on the relation between kappa calculus and probabilistic reasoning"

Connected through hyperedge HE-2 (induced by source paper "a variational approximation for bayesian networks with discrete and continuous latent variables"):
  with members:
    - "approximations in bayesian belief universe for knowledge based systems"
    - "nonuniform dynamic discretization in hybrid networks"
    - "inference using message propagation and topology transformation in vector gaussian continuous networks"
    - "optimal junction trees"
    - "simulation approaches to general probabilistic inference on belief networks"
    - "a sufficiently fast algorithm for finding close to optimal junction trees"
    - "variational probabilistic inference and the qmr dt network"

Connected through hyperedge HE-3 (induced by source paper "topological parameters for time space tradeoff"):
  with members:
    - "graphical models for preference and utility"
    - "a probabilistic approach to hierarchical model based diagnosis"
    - "nested junction trees"
    - "an evaluation of structural parameters for probabilistic reasoning results on benchmark circuits"
    - "optimal junction trees"
    - "a sufficiently fast algorithm for finding close to optimal junction trees"

Connected through hyperedge HE-4 (induced by source paper "logarithmic time parallel bayesian inference"):
  with members:
    - "logarithmic time updates and queries in probabilistic networks"
    - "directed reduction algorithms and decomposable graphs"
    - "evidence absorption and propagation through evidence reversals"
    - "parallelizing probabilistic inference some early explorations"

Connected through hyperedge HE-5 (induced by source paper "clustering without thinking about triangulation"):
  with members:
    - "optimal junction trees"

Connected through hyperedge HE-6 (induced by source paper "cutset sampling for bayesian networks"):
  with members:
    - "hugs combining exact inference and gibbs sampling in junction trees"
    - "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"
    - "an importance sampling algorithm based on evidence pre propagation"

Connected through hyperedge HE-7 (induced by source paper "randomized algorithms for the loop cutset problem"):
  with members:
    - "bounded conditioning flexible inference for decisions under scarce resources"
    - "ideal reformulation of belief networks"
    - "on the logic of causal models"
    - "causal networks semantics and expressiveness"

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)