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 characterization of the dirichlet distribution with application to learning bayesian networks"
  Abstract: We provide a new characterization of the Dirichlet distribution. This characterization implies that under assumptions made by several previous authors for learning belief networks, a Dirichlet prior on the parameters is inevitable.

Connected through hyperedge HE-1 (induced by source paper "learning bayesian networks the combination of knowledge and statistical data"):
  with members:
    - "causality in bayesian belief networks"
    - "a definition and graphical representation for causality"
    - "an algorithm for the construction of bayesian network structures from data"
    - "a transformational characterization of equivalent bayesian network structures"
    - "a construction of bayesian networks from databases based on an mdl principle"
    - "using causal information and local measures to learn bayesian networks"
    - "local expression languages for probabilistic dependence a preliminary report"
    - "deriving a minimal i map of a belief network relative to a target ordering of its nodes"

Connected through hyperedge HE-2 (induced by source paper "asymptotic model selection for directed networks with hidden variables"):
  with members:
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "efficient approximations for the marginal likelihood of incomplete data given a bayesian network"
    - "a construction of bayesian networks from databases based on an mdl principle"
    - "using causal information and local measures to learn bayesian networks"
    - "a bayesian approach to learning causal networks"
    - "mean field theory for sigmoid belief networks"
    - "operations for learning with graphical models"

Connected through hyperedge HE-3 (induced by source paper "learning bayesian networks a unification for discrete and gaussian domains"):
  with members:
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "a transformational characterization of equivalent bayesian network structures"
    - "learning gaussian networks"

Connected through hyperedge HE-4 (induced by source paper "causal discovery from a mixture of experimental and observational data"):
  with members:
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "a bayesian approach to learning causal networks"

Connected through hyperedge HE-5 (induced by source paper "learning continuous time bayesian networks"):
  with members:
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "continuous time bayesian networks"

Connected through hyperedge HE-6 (induced by source paper "searching for bayesian network structures in the space of restricted acyclic partially directed graphs"):
  with members:
    - "an algorithm for the construction of bayesian network structures from data"
    - "the recovery of causal poly trees from statistical data"
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "learning bayesian networks with local structure"
    - "causal inference and causal explanation with background knowledge"
    - "a transformational characterization of equivalent bayesian network structures"
    - "causal networks semantics and expressiveness"
    - "learning bayesian network structure from massive datasets the sparse candidate algorithm"

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)