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: "learning bayesian networks a unification for discrete and gaussian domains"
  Abstract: We examine Bayesian methods for learning Bayesian networks from a combination of prior knowledge and statistical data. In particular, we unify the approaches we presented at last year's conference for discrete and Gaussian domains. We derive a general Bayesian scoring metric, appropriate for both domains. We then use this metric in combination with well-known statistical facts about the Dirichlet and normal--Wishart distributions to derive our metrics for discrete and Gaussian domains.

Connected through hyperedge HE-1 (induced by source paper "scaling up hybrid probabilistic inference with logical and arithmetic constraints via message passing"):
  with members:
    - "efficient search based weighted model integration"
    - "probabilistic inference modulo theories"
    - "a comparison of lauritzen spiegelhalter hugin and shenoy shafer architectures for computing marginals of probability distributions"

Connected through hyperedge HE-2 (induced by source paper "hybrid probabilistic inference with logical constraints tractability and message passing"):
  with members:
    - "efficient search based weighted model integration"
    - "probabilistic inference modulo theories"

Connected through hyperedge HE-3 (induced by source paper "deep neural programs for adaptive control in cyber physical systems"):
  with members:
    - "delta complete analysis for bounded reachability of hybrid systems"

Connected through hyperedge HE-4 (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-5 (induced by source paper "gaussian process networks"):
  with members:
    - "parameter priors for directed acyclic graphical models and the characterization of several probability distributions"
    - "structure and parameter learning for causal independence and causal interaction models"
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "learning mixtures of dag models"
    - "theory refinement on bayesian networks"
    - "being bayesian about network structure"

Connected through hyperedge HE-6 (induced by source paper "parameter priors for directed acyclic graphical models and the characterization of several probability distributions"):
  with members:
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "a transformational characterization of equivalent bayesian network structures"
    - "sequential update of bayesian network structure"
    - "learning gaussian networks"
    - "operations for learning with graphical models"

Connected through hyperedge HE-7 (induced by source paper "being bayesian about network structure"):
  with members:
    - "a tutorial on learning with bayesian networks"
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "data analysis with bayesian networks a bootstrap approach"
    - "theory refinement on bayesian networks"

Connected through hyperedge HE-8 (induced by source paper "a characterization of the dirichlet distribution with application to learning bayesian networks"):
  with members:
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "learning gaussian networks"
    - "theory refinement on bayesian networks"
    - "operations for learning with graphical 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)