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: "induction of selective bayesian classifiers"
  Abstract: In this paper, we examine previous work on the naive Bayesian classifier and review its limitations, which include a sensitivity to correlated features. We respond to this problem by embedding the naive Bayesian induction scheme within an algorithm that c arries out a greedy search through the space of features. We hypothesize that this approach will improve asymptotic accuracy in domains that involve correlated features without reducing the rate of learning in ones that do not. We report experimental results on six natural domains, including comparisons with decision-tree induction, that support these hypotheses. In closing, we discuss other approaches to extending naive Bayesian classifiers and outline some directions for future research.

Connected through hyperedge HE-1 (induced by source paper "fraud uncollectible debt detection using a bayesian network based learning system a rare binary outcome with mixed data structures"):
  with members:
    - "value of evidence on influence diagrams"
    - "an entropy based learning algorithm of bayesian conditional trees"
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "evidence absorption and propagation through evidence reversals"

Connected through hyperedge HE-2 (induced by source paper "comparing bayesian network classifiers"):
  with members:
    - "learning bayesian nets that perform well"

Connected through hyperedge HE-3 (induced by source paper "learning selectively conditioned forest structures with applications to dbns and classification"):
  with members:
    - "tractable bayesian learning of tree belief networks"
    - "learning the structure of dynamic probabilistic networks"
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "theory refinement on bayesian networks"
    - "being bayesian about network structure"
    - "conditional chow liu tree structures for modeling discrete valued vector time series"

Connected through hyperedge HE-4 (induced by source paper "bayesian network classifiers in a high dimensional framework"):
  with members:
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "classifier learning with supervised marginal likelihood"
    - "analysing sensitivity data from probabilistic networks"

Connected through hyperedge HE-5 (induced by source paper "evaluation of the performance of the markov blanket bayesian classifier algorithm"):
  with members:
    - "a tutorial on learning with bayesian networks"
    - "comparing bayesian network classifiers"
    - "learning bayesian networks from incomplete databases"

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.LG(Machine Learning)