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: "An investigation of noise-tolerant relational concept learning algorithms"
  Abstract: We discuss the types of noise that may occur in relational learning systems and describe two approaches to addressing noise in a relational concept learning algorithm. We then evaluate each approach experimentally.

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"
    - "Fossil: A Robust Relational Learner"
    - "Top-Down Pruning in Relational Learning"
    - "Generating Declarative Language Bias for Top-Down ILP Algorithms"

Connected through hyperedge HE-3 (induced by source paper "Incremental Reduced Error Pruning"):
  with members:
    - "Rule Induction with CN2: Some Recent Improvements"
    - "Mingers, 1989 J. Mingers. An empirical comparison of pruning methods for decision tree induction. Machine"

Connected through hyperedge HE-4 (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"
    - "Fossil: A Robust Relational Learner"
    - "A Comparison of Pruning Methods for Relational Concept Learning"

Connected through hyperedge HE-5 (induced by source paper "Utilizing Prior Concepts for Learning"):
  with members:
    - "Evaluation and Selection of Biases in Machine Learning"
    - "The Structure-Mapping Engine: Algorithm and Examples"

Connected through hyperedge HE-6 (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"
    - "A Comparison of Pruning Methods for Relational Concept Learning"
    - "Top-Down Pruning in Relational Learning"

Connected through hyperedge HE-7 (induced by source paper "Stochastic search in inductive concept learning"):
  with members:
    - "Rule Induction with CN2: Some Recent Improvements"
    - "Mingers, 1989 J. Mingers. An empirical comparison of pruning methods for decision tree induction. Machine"
    - "Producing More Comprehensible Models While Retaining Their Performance"
    - "Linear Space Induction in First Order Logic with RELIEFF"
    - "SFOIL: Stochastic Approach to Inductive Logic Programming"
    - "An application of ILP in a musical database: learning to compose the two-voice counterpoint"

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