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: "Top-Down Pruning in Relational Learning"
  Abstract: Pruning is an effective method for dealing with noise in Machine Learning. Recently pruning algorithms, in particular Reduced Error Pruning, have also attracted interest in the field of Inductive Logic Programming. However, it has been shown that these methods can be very inefficient, because most of the time is wasted for generating clauses that explain noisy examples and subsequently pruning these clauses. We introduce a new method which searches for good theories in a top-down fashion to get a better starting point for the pruning algorithm. Experiments show that this approach can significantly lower the complexity of the task without losing predictive accuracy.

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 "A Comparison of Pruning Methods for Relational Concept 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"
    - "Generating Declarative Language Bias for Top-Down ILP Algorithms"

Connected through hyperedge HE-3 (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"
    - "A Comparison of Pruning Methods for Relational Concept Learning"
    - "Stochastic search in inductive concept learning"

Connected through hyperedge HE-4 (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"
    - "A Comparison of Pruning Methods for Relational Concept 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