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: "on the use of skeletons when learning in bayesian networks"
  Abstract: In this paper, we present a heuristic operator which aims at simultaneously optimizing the orientations of all the edges in an intermediate Bayesian network structure during the search process. This is done by alternating between the space of directed acyclic graphs (DAGs) and the space of skeletons. The found orientations of the edges are based on a scoring function rather than on induced conditional independences. This operator can be used as an extension to commonly employed search strategies. It is evaluated in experiments with artificial and real-world data.

Connected through hyperedge HE-1 (induced by source paper "ordering based search a simple and effective algorithm for learning bayesian networks"):
  with members:
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "learning bayesian network structure from massive datasets the sparse candidate algorithm"
    - "cached sufficient statistics for efficient machine learning with large datasets"

Connected through hyperedge HE-2 (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)