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 transformational characterization of equivalent bayesian network structures"
  Abstract: We present a simple characterization of equivalent Bayesian network structures based on local transformations. The significance of the characterization is twofold. First, we are able to easily prove several new invariant properties of theoretical interest for equivalent structures. Second, we use the characterization to derive an efficient algorithm that identifies all of the compelled edges in a structure. Compelled edge identification is of particular importance for learning Bayesian network structures from data because these edges indicate causal relationships when certain assumptions hold.

Connected through hyperedge HE-1 (induced by source paper "a tutorial on learning with bayesian networks"):
  with members:
    - "query dags a practical paradigm for implementing belief network inference"
    - "approximations in bayesian belief universe for knowledge based systems"
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "asymptotic model selection for directed networks with hidden variables"
    - "directed reduction algorithms and decomposable graphs"
    - "computing upper and lower bounds on likelihoods in intractable networks"
    - "theory refinement on bayesian networks"
    - "a bayesian approach to learning causal networks"

Connected through hyperedge HE-2 (induced by source paper "selective greedy equivalence search finding optimal bayesian networks using a polynomial number of score evaluations"):
  with members:
    - "learning bayesian network structure from massive datasets the sparse candidate algorithm"
    - "parameterized complexity results for exact bayesian network structure learning"
    - "large sample learning of bayesian networks is np hard"
    - "a simple approach for finding the globally optimal bayesian network structure"
    - "finding optimal bayesian networks"
    - "on finding optimal polytrees"
    - "pac learning bounded tree width graphical models"
    - "finding a path is harder than finding a tree"

Connected through hyperedge HE-3 (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 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-4 (induced by source paper "parameterized complexity results for exact bayesian network structure learning"):
  with members:
    - "on the hardness of losing weight"
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "learning bayesian network structure from massive datasets the sparse candidate algorithm"
    - "a complete anytime algorithm for treewidth"
    - "a simple approach for finding the globally optimal bayesian network structure"
    - "algorithms and complexity results for exact bayesian structure learning"
    - "don t be strict in local search"
    - "ordering based search a simple and effective algorithm for learning bayesian networks"

Connected through hyperedge HE-5 (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"
    - "sequential update of bayesian network structure"
    - "learning gaussian networks"
    - "learning bayesian networks a unification for discrete and gaussian domains"
    - "operations for learning with graphical models"

Connected through hyperedge HE-6 (induced by source paper "learning bayesian network structure from massive datasets the sparse candidate algorithm"):
  with members:
    - "a tutorial on learning with bayesian networks"
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "fraud uncollectible debt detection using a bayesian network based learning system a rare binary outcome with mixed data structures"
    - "cached sufficient statistics for efficient machine learning with large datasets"

Connected through hyperedge HE-7 (induced by source paper "enumerating markov equivalence classes of acyclic digraph models"):
  with members:
    - "an algorithm for deciding if a set of observed independencies has a causal explanation"
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "operations for learning with graphical models"

Connected through hyperedge HE-8 (induced by source paper "data analysis with bayesian networks a bootstrap approach"):
  with members:
    - "a tutorial on learning with bayesian networks"
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "causal inference and causal explanation with background knowledge"

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)