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 bayesian method for constructing bayesian belief networks from databases"
  Abstract: This paper presents a Bayesian method for constructing Bayesian belief networks from a database of cases. Potential applications include computer-assisted hypothesis testing, automated scientific discovery, and automated construction of probabilistic expert systems. Results are presented of a preliminary evaluation of an algorithm for constructing a belief network from a database of cases. We relate the methods in this paper to previous work, and we discuss open problems.

Connected through hyperedge HE-1 (induced by source paper "needmining identifying micro blog data containing customer needs"):
  with members:
    - "estimating continuous distributions in bayesian classifiers"
    - "smote synthetic minority over sampling technique"

Connected through hyperedge HE-2 (induced by source paper "efficient computational strategies to learn the structure of probabilistic graphical models of cumulative phenomena"):
  with members:
    - "causal data science for financial stress testing"
    - "modeling cumulative biological phenomena with suppes bayes causal networks"
    - "efficient simulation of financial stress testing scenarios with suppes bayes causal networks"
    - "exposing the probabilistic causal structure of discrimination"
    - "parallel implementation of efficient search schemes for the inference of cancer progression models"
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "theory refinement on bayesian networks"
    - "ordering based search a simple and effective algorithm for learning bayesian networks"

Connected through hyperedge HE-3 (induced by source paper "causal mechanism based model construction"):
  with members:
    - "causality in bayesian belief networks"
    - "network fragments representing knowledge for constructing probabilistic models"
    - "a generalization of the noisy or model"
    - "object oriented bayesian networks"
    - "spook a system for probabilistic object oriented knowledge representation"

Connected through hyperedge HE-4 (induced by source paper "fast learning from sparse data"):
  with members:
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "a bayesian approach to learning bayesian networks with local structure"
    - "theory refinement on bayesian networks"
    - "cached sufficient statistics for efficient machine learning with large datasets"

Connected through hyperedge HE-5 (induced by source paper "sensitivity analysis for probability assessments in bayesian networks"):
  with members:
    - "advances in probabilistic reasoning"
    - "theory refinement on bayesian networks"

Connected through hyperedge HE-6 (induced by source paper "a construction of bayesian networks from databases based on an mdl principle"):
  with members:
    - "from relational databases to belief networks"

Connected through hyperedge HE-7 (induced by source paper "using causal information and local measures to learn bayesian networks"):
  with members:
    - "the recovery of causal poly trees from statistical data"

Connected through hyperedge HE-8 (induced by source paper "a bayesian method reexamined"):
  with members:
    - "an algorithm for the construction of bayesian network structures from data"

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)