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 "a variational approximation for bayesian networks with discrete and continuous latent variables"):
  Abstract: We show how to use a variational approximation to the logistic function to perform approximate inference in Bayesian networks containing discrete nodes with continuous parents. Essentially, we convert the logistic function to a Gaussian, which facilitates exact inference, and then iteratively adjust the variational parameters to improve the quality of the approximation. We demonstrate experimentally that this approximation is faster and potentially more accurate than sampling. We also introduce a simple new technique for handling evidence, which allows us to handle arbitrary distributions on observed nodes, as well as achieving a significant speedup in networks with discrete variables of large cardinality.

Includes members:
  Member "approximations in bayesian belief universe for knowledge based systems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a tutorial on learning with bayesian networks")
  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 "inference using message propagation and topology transformation in vector gaussian continuous networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "mix nets factored mixtures of gaussians in bayesian networks with mixed continuous and discrete variables")
  Member "optimal junction trees":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning the relation between code features and code transforms with structured prediction")
  Member "simulation approaches to general probabilistic inference on belief networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "constraint based inference in probabilistic logic programs")
  Member "global conditioning for probabilistic inference in belief networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "query dags a practical paradigm for implementing belief network inference")
  Member "a sufficiently fast algorithm for finding close to optimal junction trees":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "topological parameters for time space tradeoff")
  Member "variational probabilistic inference and the qmr dt network":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "the accuracy vs coverage trade off in patient facing diagnosis models")

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)