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: "Decision Tree Induction: How Effective is the Greedy Heuristic?"
  Abstract: Most existing decision tree systems use a greedy approach to induce trees | locally optimal splits are induced at every node of the tree. Although the greedy approach is suboptimal, it is believed to produce reasonably good trees. In the current work, we attempt to verify this belief. We quantify the goodness of greedy tree induction empirically, using the popular decision tree algorithms, C4.5 and CART. We induce decision trees on thousands of synthetic data sets and compare them to the corresponding optimal trees, which in turn are found using a novel map coloring idea. We measure the effect on greedy induction of variables such as the underlying concept complexity, training set size, noise and dimensionality. Our experiments show, among other things, that the expected classification cost of a greedily induced tree is consistently very close to that of the optimal tree.

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 "Machine Learning Bias, Statistical Bias, and Statistical Variance of Decision Tree Algorithms"):
  with members:
    - "A Statistical Approach to Decision Tree Modeling"
    - "Error-Correcting Output Codes: A General Method for Improving Multiclass Inductive Learning Programs"
    - "Bias Plus Variance Decomposition for Zero-One Loss Functions"

Connected through hyperedge HE-3 (induced by source paper "A System for Induction of Oblique Decision Trees"):
  with members:
    - "Decision Tree Function Approximation in Reinforcement Learning"
    - "A hierarchical ensemble of decision trees applied to classifying data from a psychological experiment"
    - "Lazy Induction Triggered by CBR"
    - "Induction of Oblique Decision Trees"
    - "Geometry in Learning"
    - "Lookahead and Pathology in Decision Tree Induction"
    - "A Decision Tree System for Finding Genes in DNA"
    - "LEARNING BY USING DYNAMIC FEATURE COMBINATION AND SELECTION"

Connected through hyperedge HE-4 (induced by source paper "Lookahead and Pathology in Decision Tree Induction"):
  with members:
    - "A System for Induction of Oblique Decision Trees"
    - "Induction of Oblique Decision Trees"
    - "Exploring the Decision Forest: An Empirical Investigation of Occam's Razor in Decision Tree 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