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: "asymptotic model selection for naive bayesian networks"
  Abstract: We develop a closed form asymptotic formula to compute the marginal likelihood of data given a naive Bayesian network model with two hidden states and binary features. This formula deviates from the standard BIC score. Our work provides a concrete example that the BIC score is generally not valid for statistical models that belong to a stratified exponential family. This stands in contrast to linear and curved exponential families, where the BIC score has been proven to provide a correct approximation for the marginal likelihood.

Connected through hyperedge HE-1 (induced by source paper "algebraic statistics in model selection"):
  with members:
    - "on the geometry of bayesian graphical models with hidden variables"
    - "graphical models and exponential families"
    - "dimension correction for hierarchical latent class models"
    - "automated analytic asymptotic evaluation of the marginal likelihood for latent models"

Connected through hyperedge HE-2 (induced by source paper "automated analytic asymptotic evaluation of the marginal likelihood for latent models"):
  with members:
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "on the geometry of bayesian graphical models with hidden variables"
    - "asymptotic model selection for directed networks with hidden variables"

Connected through hyperedge HE-3 (induced by source paper "a widely applicable bayesian information criterion"):
  with members:
    - "asymptotic learning curve and renormalizable condition in statistical learning theory"
    - "asymptotic equivalence of bayes cross validation and widely applicable information criterion in singular learning theory"

Connected through hyperedge HE-4 (induced by source paper "asymptotic equivalence of bayes cross validation and widely applicable information criterion in singular learning theory"):
  with members:
    - "equations of states in statistical learning for an unrealizable and regular case"
    - "asymptotic learning curve and renormalizable condition in statistical learning theory"
    - "a limit theorem in singular regression problem"
    - "equations of states in singular statistical estimation"

Connected through hyperedge HE-5 (induced by source paper "effective dimensions of hierarchical latent class models"):
  with members:
    - "on the geometry of bayesian graphical models with hidden variables"
    - "asymptotic model selection for directed networks with hidden variables"
    - "dimension correction for hierarchical latent class models"

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)