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: "an algorithm for the construction of bayesian network structures from data"
  Abstract: Previous algorithms for the construction of Bayesian belief network structures from data have been either highly dependent on conditional independence (CI) tests, or have required an ordering on the nodes to be supplied by the user. We present an algorithm that integrates these two approaches - CI tests are used to generate an ordering on the nodes from the database which is then used to recover the underlying Bayesian network structure using a non CI based method. Results of preliminary evaluation of the algorithm on two networks (ALARM and LED) are presented. We also discuss some algorithm performance issues and open problems.

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"
    - "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"
    - "learning gaussian networks"

Connected through hyperedge HE-2 (induced by source paper "causal inference in the presence of latent variables and selection bias"):
  with members:
    - "strong completeness and faithfulness in bayesian networks"
    - "learning bayesian networks the combination of knowledge and statistical data"

Connected through hyperedge HE-3 (induced by source paper "a bayesian method reexamined"):
  with members:
    - "a bayesian method for constructing bayesian belief networks from databases"

Connected through hyperedge HE-4 (induced by source paper "a robust independence test for constraint based learning of causal structure"):
  with members:
    - "learning bayesian network structure from massive datasets the sparse candidate algorithm"
    - "improved learning of bayesian networks"

Connected through hyperedge HE-5 (induced by source paper "searching for bayesian network structures in the space of restricted acyclic partially directed graphs"):
  with members:
    - "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"
    - "improved learning of bayesian networks"

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)