human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent groups of papers sharing a common citing paper.

Details of the hypergraph:
Center node: "Induction of One-Level Decision Trees"
  Abstract: In recent years, researchers have made considerable progress on the worst-case analysis of inductive learning tasks, but for theoretical results to have impact on practice, they must deal with the average case. In this paper we present an average-case analysis of a simple algorithm that induces one-level decision trees for concepts defined by a single relevant attribute. Given knowledge about the number of training instances, the number of irrelevant attributes, the amount of class and attribute noise, and the class and attribute distributions, we derive the expected classification accuracy over the entire instance space. We then examine the predictions of this analysis for different settings of these domain parameters, comparing them to exper imental results to check our reasoning.

Connected through hyperedge HE-1 (induced by source paper "Mingers, 1989 J. Mingers. An empirical comparison of pruning methods for decision tree induction. Machine"):
  with members:
    - "Pessimistic decision tree pruning based on tree size"
    - "Multivariate Decision Trees"
    - "Dynamic Automatic Model Selection"
    - "Finding new rules for incomplete theories: Explicit biases for induction with contextual information. In Proceedings"
    - "Induction of Oblique Decision Trees"
    - "Data Analyses Using Simulated Breeding and Inductive Learning Methods"
    - "Learning Decision Trees from Decision Rules:"
    - "The Estimation of Probabilities in Attribute Selection Measures for Decision Tree Induction"

Connected through hyperedge HE-2 (induced by source paper "Average-Case Analysis of a Nearest Neighbor Algorithm"):
  with members:
    - "Oblivious Decision Trees and Abstract Cases"
    - "Towards a Better Understanding of Memory-Based Reasoning Systems"
    - "An Analysis of Bayesian Classifiers (1988), involves the formulation of average-case models for specific algorithms"
    - "Inductive Bias in Case-Based Reasoning Systems"
    - "PAC Analyses of a `Similarity Learning' IBL Algorithm"

Connected through hyperedge HE-3 (induced by source paper "An Analysis of Bayesian Classifiers (1988), involves the formulation of average-case models for specific algorithms"):
  with members:
    - "Towards a Better Understanding of Memory-Based Reasoning Systems"
    - "Computational Learning in Humans and Machines"
    - "Average-Case Analysis of a Nearest Neighbor Algorithm"
    - "A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection"
    - "Mining and Model Simplicity: A Case Study in Diagnosis"
    - "Issues in the Integration of Data Mining and Data Visualization Visualizing the Simple Bayesian Classifier"

Connected through hyperedge HE-4 (induced by source paper "In Defense of C4.5: Notes on Learning One-Level Decision Trees"):
  with members:
    - "Exploring the Decision Forest: An Empirical Investigation of Occam's Razor in Decision Tree Induction"
    - "Learning to Represent Codons: A Challenge Problem for Constructive Induction"

Question: Please tell me which class the center node belongs to. The 7 classes are: Neural_Networks, Reinforcement_Learning, Probabilistic_Methods, Theory, Genetic_Algorithms, Case_Based, Rule_Learning. Directly output the class name.

gpt: Theory