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 Oblique Decision Trees"
  Abstract: This paper introduces a randomized technique for partitioning examples using oblique hyperplanes. Standard decision tree techniques, such as ID3 and its descendants, partition a set of points with axis-parallel hyper-planes. Our method, by contrast, attempts to find hyperplanes at any orientation. The purpose of this more general technique is to find smaller but equally accurate decision trees than those created by other methods. We have tested our algorithm on both real and simulated data, and found that in some cases it produces surprisingly small trees without losing predictive accuracy. Small trees allow us, in turn, to obtain simple qualitative descriptions of each problem domain.

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"
    - "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"
    - "Learning Classification Trees"

Connected through hyperedge HE-2 (induced by source paper "PATTERN RECOGNITION VIA LINEAR PROGRAMMING THEORY AND APPLICATION TO MEDICAL DIAGNOSIS"):
  with members:
    - "Misclassification Minimization"
    - "Misclassification Minimization"
    - "Misclassification Minimization"
    - "Bilinear Separation of Two Sets in n-Space"
    - "Mathematical Programming in Neural Networks"
    - "Geometry in Learning"

Connected through hyperedge HE-3 (induced by source paper "Learning When Reformulation is Appropriate for Iterative Design"):
  with members:
    - "Learning Prototype-Selection Rules for Case-Based Iterative Design seen as a case-based reasoning system [4], in"
    - "A Transformation System for Interactive Reformulation of Design Optimization Strategies"

Connected through hyperedge HE-4 (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"
    - "Geometry in Learning"
    - "Lookahead and Pathology in Decision Tree Induction"
    - "A Decision Tree System for Finding Genes in DNA"
    - "Decision Tree Induction: How Effective is the Greedy Heuristic?"
    - "LEARNING BY USING DYNAMIC FEATURE COMBINATION AND SELECTION"

Connected through hyperedge HE-5 (induced by source paper "Misclassification Minimization"):
  with members:
    - "Book Review  Introduction to the Theory of Neural Computation Reviewed by: 2"
    - "PATTERN RECOGNITION VIA LINEAR PROGRAMMING THEORY AND APPLICATION TO MEDICAL DIAGNOSIS"
    - "Bilinear Separation of Two Sets in n-Space"

Connected through hyperedge HE-6 (induced by source paper "An Improved Algorithm for Incremental Induction of Decision Trees"):
  with members:
    - "Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,"
    - "Theory Revision in Fault Hierarchies"
    - "Integration of Case-Based Reasoning and Neural Networks Approaches for Classification"
    - "Lazy Induction Triggered by CBR"
    - "Learning Classification Trees"
    - "Relating Relational Learning Algorithms"
    - "Geometric Comparison of Classifications and Rule Sets*"
    - "What Daimler-Benz has learned as an industrial partner from the Machine Learning Project StatLog"

Connected through hyperedge HE-7 (induced by source paper "Lookahead and Pathology in Decision Tree Induction"):
  with members:
    - "A System for Induction of Oblique Decision Trees"
    - "Exploring the Decision Forest: An Empirical Investigation of Occam's Razor in Decision Tree Induction"
    - "Decision Tree Induction: How Effective is the Greedy Heuristic?"

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