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: "efficient stepwise selection in decomposable models"
  Abstract: In this paper, we present an efficient way of performing stepwise selection in the class of decomposable models. The main contribution of the paper is a simple characterization of the edges that canbe added to a decomposable model while keeping the resulting model decomposable and an efficient algorithm for enumerating all such edges for a given model in essentially O(1) time per edge. We also discuss how backward selection can be performed efficiently using our data structures.We also analyze the complexity of the complete stepwise selection procedure, including the complexity of choosing which of the eligible dges to add to (or delete from) the current model, with the aim ofminimizing the Kullback-Leibler distance of the resulting model from the saturated model for the data.

Connected through hyperedge HE-1 (induced by source paper "structure learning using forced pruning"):
  with members:
    - "maximum likelihood bounded tree width markov networks"
    - "beyond log supermodularity lower bounds and the bethe partition function"

Connected through hyperedge HE-2 (induced by source paper "learning factor graphs in polynomial time sample complexity"):
  with members:
    - "maximum likelihood bounded tree width markov networks"
    - "on the sample complexity of learning bayesian networks"
    - "efficiently inducing features of conditional random fields"
    - "finding optimal bayesian networks"
    - "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 "convex relaxations for learning bounded treewidth decomposable graphs"):
  with members:
    - "maximum likelihood bounded tree width markov networks"
    - "generalized sequential tree reweighted message passing"
    - "ordering based search a simple and effective algorithm for learning bayesian networks"
    - "pac learning bounded tree width graphical models"
    - "near optimal nonmyopic value of information in graphical models"

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)