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 directed networks with hidden variables"
  Abstract: We extend the Bayesian Information Criterion (BIC), an asymptotic approximation for the marginal likelihood, to Bayesian networks with hidden variables. This approximation can be used to select models given large samples of data. The standard BIC as well as our extension punishes the complexity of a model according to the dimension of its parameters. We argue that the dimension of a Bayesian network with hidden variables is the rank of the Jacobian matrix of the transformation between the parameters of the network and the parameters of the observable variables. We compute the dimensions of several networks including the naive Bayes model with a hidden root node.

Connected through hyperedge HE-1 (induced by source paper "a tutorial on learning with bayesian networks"):
  with members:
    - "query dags a practical paradigm for implementing belief network inference"
    - "approximations in bayesian belief universe for knowledge based systems"
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "directed reduction algorithms and decomposable graphs"
    - "a transformational characterization of equivalent bayesian network structures"
    - "computing upper and lower bounds on likelihoods in intractable networks"
    - "theory refinement on bayesian networks"
    - "a bayesian approach to learning causal networks"

Connected through hyperedge HE-2 (induced by source paper "a survey on latent tree models and applications"):
  with members:
    - "inferring parameters and structure of latent variable models by variational bayes"
    - "learning the dimensionality of hidden variables"
    - "spectral methods for learning multivariate latent tree structure"
    - "learning high dimensional markov forest distributions analysis of error rates"
    - "a spectral algorithm for learning hidden markov models"
    - "latent tree models and approximate inference in bayesian networks"
    - "learning nonsingular phylogenies and hidden markov models"
    - "effective dimensions of hierarchical latent class models"

Connected through hyperedge HE-3 (induced by source paper "the bayesian structural em algorithm"):
  with members:
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "a bayesian approach to learning bayesian networks with local structure"
    - "learning bayesian networks with local structure"
    - "context specific independence in bayesian networks"
    - "mean field theory for sigmoid belief networks"

Connected through hyperedge HE-4 (induced by source paper "a bayesian method for causal modeling and discovery under selection"):
  with members:
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "causal discovery from a mixture of experimental and observational data"
    - "evidence absorption and propagation through evidence reversals"
    - "learning gaussian networks"
    - "causal inference in the presence of latent variables and selection bias"

Connected through hyperedge HE-5 (induced by source paper "structure and parameter learning for causal independence and causal interaction models"):
  with members:
    - "a bayesian approach to learning bayesian networks with local structure"
    - "mean field theory for sigmoid belief networks"
    - "exploiting causal independence in bayesian network inference"
    - "operations for learning with graphical models"

Connected through hyperedge HE-6 (induced by source paper "graphical models and exponential families"):
  with members:
    - "a bayesian approach to learning bayesian networks with local structure"
    - "learning bayesian networks with local structure"
    - "an alternative markov property for chain graphs"

Connected through hyperedge HE-7 (induced by source paper "efficient approximations for the marginal likelihood of incomplete data given a bayesian network"):
  with members:
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "operations for learning with graphical models"

Connected through hyperedge HE-8 (induced by source paper "sequential update of bayesian network structure"):
  with members:
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "theory refinement on 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)