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: "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.

Connected through hyperedge HE-1 (induced by source paper "learning probabilistic logic programs in continuous domains"):
  with members:
    - "quantifying program bias"
    - "approximate counting in smt and value estimation for probabilistic programs"
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "inference in probabilistic logic programs using weighted cnf s"
    - "the magic of logical inference in probabilistic programming"
    - "probabilistic reasoning with answer sets"

Connected through hyperedge HE-2 (induced by source paper "exact inference in networks with discrete children of continuous parents"):
  with members:
    - "nonuniform dynamic discretization in hybrid networks"
    - "simulation approaches to general probabilistic inference on belief networks"
    - "inference in hybrid networks theoretical limits and practical algorithms"
    - "variational approximations between mean field theory and the junction tree algorithm"

Connected through hyperedge HE-3 (induced by source paper "variational approximations between mean field theory and the junction tree algorithm"):
  with members:
    - "variational probabilistic inference and the qmr dt network"
    - "mean field theory for sigmoid belief networks"

Connected through hyperedge HE-4 (induced by source paper "inference in hybrid bayesian networks using mixtures of gaussians"):
  with members:
    - "mixtures of gaussians and minimum relative entropy techniques for modeling continuous uncertainties"
    - "nonuniform dynamic discretization in hybrid networks"
    - "inference in hybrid networks theoretical limits and practical algorithms"
    - "exact inference in networks with discrete children of continuous parents"
    - "approximate inference algorithms for hybrid bayesian networks with discrete constraints"
    - "hybrid bayesian networks with linear deterministic variables"

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)