human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "inference in hybrid bayesian networks using mixtures of gaussians"):
  Abstract: The main goal of this paper is to describe a method for exact inference in general hybrid Bayesian networks (BNs) (with a mixture of discrete and continuous chance variables). Our method consists of approximating general hybrid Bayesian networks by a mixture of Gaussians (MoG) BNs. There exists a fast algorithm by Lauritzen-Jensen (LJ) for making exact inferences in MoG Bayesian networks, and there exists a commercial implementation of this algorithm. However, this algorithm can only be used for MoG BNs. Some limitations of such networks are as follows. All continuous chance variables must have conditional linear Gaussian distributions, and discrete chance nodes cannot have continuous parents. The methods described in this paper will enable us to use the LJ algorithm for a bigger class of hybrid Bayesian networks. This includes networks with continuous chance nodes with non-Gaussian distributions, networks with no restrictions on the topology of discrete and continuous variables, networks with conditionally deterministic variables that are a nonlinear function of their continuous parents, and networks with continuous chance variables whose variances are functions of their parents.

Includes members:
  Member "mixtures of gaussians and minimum relative entropy techniques for modeling continuous uncertainties":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "laplace s method approximations for probabilistic inference in belief networks with continuous variables")
  Member "nonuniform dynamic discretization in hybrid networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "region based approximation for high dimensional bayesian network models")
  Member "a variational approximation for bayesian networks with discrete and continuous latent variables":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning probabilistic logic programs in continuous domains")
  Member "inference in hybrid networks theoretical limits and practical algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "efficient online learning for optimizing value of information theory and application to interactive troubleshooting")
  Member "exact inference in networks with discrete children of continuous parents":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "the computational power of dynamic bayesian networks")
  Member "approximate inference algorithms for hybrid bayesian networks with discrete constraints":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on the approximability of weighted model integration on dnf structures")
  Member "hybrid bayesian networks with linear deterministic variables":
    also connected through other hyperedges:
      (none)

Question: Please tell me which class the source paper 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)