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 Comparison of Pruning Methods for Relational Concept Learning"
  Abstract: Pre-Pruning and Post-Pruning are two standard methods of dealing with noise in concept learning. Pre-Pruning methods are very efficient, while Post-Pruning methods typically are more accurate, but much slower, because they have to generate an overly specific concept description first. We have experimented with a variety of pruning methods, including two new methods that try to combine and integrate pre- and post-pruning in order to achieve both accuracy and efficiency. This is verified with test series in a chess position classification task.

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 "Top-Down Pruning in Relational Learning"):
  with members:
    - "Mingers, 1989 J. Mingers. An empirical comparison of pruning methods for decision tree induction. Machine"
    - "An investigation of noise-tolerant relational concept learning algorithms"
    - "Fossil: A Robust Relational Learner"

Connected through hyperedge HE-3 (induced by source paper "Generating Declarative Language Bias for Top-Down ILP Algorithms"):
  with members:
    - "Finding Accurate Frontiers: A Knowledge-Intensive Approach to Relational Learning"
    - "Applying ILP to Diterpene Structure Elucidation from 13 C NMR Spectra"
    - "Mining for Causes of Cancer: Machine Learning Experiments at Various Levels of Detail"
    - "Top-down Induction of Logical Decision Trees"

Connected through hyperedge HE-4 (induced by source paper "An investigation of noise-tolerant relational concept learning algorithms"):
  with members:
    - "Utilizing Prior Concepts for Learning"
    - "Mingers, 1989 J. Mingers. An empirical comparison of pruning methods for decision tree induction. Machine"
    - "Incremental Reduced Error Pruning"
    - "Fossil: A Robust Relational Learner"
    - "Top-Down Pruning in Relational Learning"
    - "Stochastic search in inductive concept learning"

Connected through hyperedge HE-5 (induced by source paper "Fossil: A Robust Relational Learner"):
  with members:
    - "Concept Learning and the Problem of Small"
    - "Rule Induction with CN2: Some Recent Improvements"
    - "Mingers, 1989 J. Mingers. An empirical comparison of pruning methods for decision tree induction. Machine"
    - "Predicting Ordinal Classes in ILP"
    - "An investigation of noise-tolerant relational concept learning algorithms"
    - "Top-Down Pruning in Relational Learning"

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