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 "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.

Includes members:
  Member "learning bayesian networks the combination of knowledge and statistical data":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a tutorial on learning with bayesian networks")
  Member "efficient approximations for the marginal likelihood of incomplete data given a bayesian network":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning bayesian networks from incomplete databases")
  Member "a construction of bayesian networks from databases based on an mdl principle":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "bayesian learning of sum product networks")
  Member "using causal information and local measures to learn bayesian networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning bayesian networks the combination of knowledge and statistical data")
  Member "a characterization of the dirichlet distribution with application to learning bayesian networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning bayesian networks the combination of knowledge and statistical data")
  Member "a bayesian approach to learning causal networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a tutorial on learning with bayesian networks")
  Member "mean field theory for sigmoid belief networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a tutorial on learning with bayesian networks")
  Member "operations for learning with graphical models":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "state space abstractions for probabilistic inference a systematic review")

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.LG(Machine Learning)