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 multivariate discretization method for learning bayesian networks from mixed data"
  Abstract: In this paper we address the problem of discretization in the context of learning Bayesian networks (BNs) from data containing both continuous and discrete variables. We describe a new technique for multivariate discretization, whereby each continuous variable is discretized while taking into account its interaction with the other variables. The technique is based on the use of a Bayesian scoring metric that scores the discretization policy for a continuous variable given a BN structure and the observed data. Since the metric is relative to the BN structure currently being evaluated, the discretization of a variable needs to be dynamically adjusted as the BN structure changes.

Connected through hyperedge HE-1 (induced by source paper "learning discrete bayesian networks from continuous data"):
  with members:
    - "nonuniform dynamic discretization in hybrid networks"
    - "minimum description length induction bayesianism and kolmogorov complexity"

Connected through hyperedge HE-2 (induced by source paper "mix nets factored mixtures of gaussians in bayesian networks with mixed continuous and discrete variables"):
  with members:
    - "nonuniform dynamic discretization in hybrid networks"
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "learning bayesian networks with local structure"
    - "the anchors hierachy using the triangle inequality to survive high dimensional data"
    - "learning bayesian network structure from massive datasets the sparse candidate algorithm"
    - "inference using message propagation and topology transformation in vector gaussian continuous networks"
    - "models and selection criteria for regression and classification"

Connected through hyperedge HE-3 (induced by source paper "interpolating conditional density trees"):
  with members:
    - "mix nets factored mixtures of gaussians in bayesian networks with mixed continuous and discrete variables"
    - "learning bayesian networks with local structure"
    - "learning bayesian network structure from massive datasets the sparse candidate algorithm"
    - "learning bayesian networks a unification for discrete and gaussian domains"

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)