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: "Machine Learning and Inference"
  Abstract: Constructive induction divides the problem of learning an inductive hypothesis into two intertwined searches: onefor the best representation space, and twofor the best hypothesis in that space. In data-driven constructive induction (DCI), a learning system searches for a better representation space by analyzing the input examples (data). The presented data-driven constructive induction method combines an AQ-type learning algorithm with two classes of representation space improvement operators: constructors, and destructors. The implemented system, AQ17-DCI, has been experimentally applied to a GNP prediction problem using a World Bank database. The results show that decision rules learned by AQ17-DCI outperformed the rules learned in the original representation space both in predictive accuracy and rule simplicity.

Connected through hyperedge HE-1 (induced by source paper "Supervised and Unsupervised Discretization of Continuous Features"):
  with members:
    - "BOOSTING AND NAIVE BAYESIAN LEARNING"
    - "Error-Based and Entropy-Based Discretization of Continuous Features"
    - "Machine Learning and Inference"

Connected through hyperedge HE-2 (induced by source paper "INFERENTIAL THEORY OF LEARNING: Developing Foundations for Multistrategy Learning"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "A theory of questions and question asking"
    - "In Machine Learning: A Multistrategy Approach, Vol. IV  Macro and Micro Perspectives of Multistrategy Learning"
    - "The Use of Explicit Goals for Knowledge to Guide Inference and Learning"
    - "Issues in Goal-Driven Explanation"
    - "Context-Based Similarity Applied to Retrieval of Relevant Cases"
    - "Hybrid Learning Using Genetic Algorithms and Decision Trees for Pattern Classification"
    - "Machine Learning and Inference"

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: Rule_Learning