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: "on the geometry of bayesian graphical models with hidden variables"
  Abstract: In this paper we investigate the geometry of the likelihood of the unknown parameters in a simple class of Bayesian directed graphs with hidden variables. This enables us, before any numerical algorithms are employed, to obtain certain insights in the nature of the unidentifiability inherent in such models, the way posterior densities will be sensitive to prior densities and the typical geometrical form these posterior densities might take. Many of these insights carry over into more complicated Bayesian networks with systematic missing data.

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

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"
    - "asymptotic model selection for directed networks with hidden variables"
    - "asymptotic model selection for naive bayesian networks"

Connected through hyperedge HE-3 (induced by source paper "asymptotic model selection for naive bayesian networks"):
  with members:
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "asymptotic model selection for directed networks with hidden variables"

Connected through hyperedge HE-4 (induced by source paper "dimension correction for hierarchical latent class models"):
  with members:
    - "asymptotic model selection for directed networks with hidden variables"

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

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.LG(Machine Learning)