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: "A System for Induction of Oblique Decision Trees"
  Abstract: This article describes a new system for induction of oblique decision trees. This system, OC1, combines deterministic hill-climbing with two forms of randomization to find a good oblique split (in the form of a hyperplane) at each node of a decision tree. Oblique decision tree methods are tuned especially for domains in which the attributes are numeric, although they can be adapted to symbolic or mixed symbolic/numeric attributes. We present extensive empirical studies, using both real and artificial data, that analyze OC1's ability to construct oblique trees that are smaller and more accurate than their axis-parallel counterparts. We also examine the benefits of randomization for the construction of oblique decision trees.

Connected through hyperedge HE-1 (induced by source paper "A Support Vector Machine Approach to Decision Trees"):
  with members:
    - "Improving the Accuracy and Speed of Support Vector Machines"
    - "Parsimonious Least Norm Approximation"
    - "Massive Data Discrimination via Linear Support Vector Machines"

Connected through hyperedge HE-2 (induced by source paper "Induction of Oblique Decision Trees"):
  with members:
    - "An Improved Algorithm for Incremental Induction of Decision Trees"
    - "PATTERN RECOGNITION VIA LINEAR PROGRAMMING THEORY AND APPLICATION TO MEDICAL DIAGNOSIS"
    - "Misclassification Minimization"
    - "Misclassification Minimization"
    - "Misclassification Minimization"
    - "Mingers, 1989 J. Mingers. An empirical comparison of pruning methods for decision tree induction. Machine"
    - "Learning When Reformulation is Appropriate for Iterative Design"
    - "Lookahead and Pathology in Decision Tree Induction"

Connected through hyperedge HE-3 (induced by source paper "Decision Tree Induction: How Effective is the Greedy Heuristic?"):
  with members:
    - "Mingers, 1989 J. Mingers. An empirical comparison of pruning methods for decision tree induction. Machine"
    - "Lookahead and Pathology in Decision Tree Induction"
    - "Machine Learning Bias, Statistical Bias, and Statistical Variance of Decision Tree Algorithms"

Connected through hyperedge HE-4 (induced by source paper "LEARNING BY USING DYNAMIC FEATURE COMBINATION AND SELECTION"):
  with members:
    - "A decision-theoretic generalization of on-line learning and an application to boosting how the weight-update rule"
    - "Error-Correcting Output Codes: A General Method for Improving Multiclass Inductive Learning Programs"
    - "Generating Accurate and Diverse Members of a Neural-Network Ensemble"

Connected through hyperedge HE-5 (induced by source paper "Lazy Induction Triggered by CBR"):
  with members:
    - "An Improved Algorithm for Incremental Induction of Decision Trees"
    - "Inductive Learning and Case-Based Reasoning"

Connected through hyperedge HE-6 (induced by source paper "Decision Tree Function Approximation in Reinforcement Learning"):
  with members:
    - "References elements that can solve difficult learning control problems. on Simulation of Adaptive Behavior, pages"
    - "Generalization in Reinforcement Learning: Safely Approximating the Value Function"
    - "Generalization in Reinforcement Learning: Successful Examples Using Sparse Coarse Coding"

Connected through hyperedge HE-7 (induced by source paper "A Decision Tree System for Finding Genes in DNA"):
  with members:
    - "Finding Genes in DNA with a Hidden Markov Model"
    - "A Generalized Hidden Markov Model for the Recognition of Human Genes in DNA"
    - "A Method for Identifying Splice Sites and Translational Start Sites in"

Connected through hyperedge HE-8 (induced by source paper "Lookahead and Pathology in Decision Tree Induction"):
  with members:
    - "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