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: "Theory Refinement Combining Analytical and Empirical Methods"
  Abstract: This article describes a comprehensive approach to automatic theory revision. Given an imperfect theory, the approach combines explanation attempts for incorrectly classified examples in order to identify the failing portions of the theory. For each theory fault, correlated subsets of the examples are used to inductively generate a correction. Because the corrections are focused, they tend to preserve the structure of the original theory. Because the system starts with an approximate domain theory, in general fewer training examples are required to attain a given level of performance (classification accuracy) compared to a purely empirical system. The approach applies to classification systems employing a propositional Horn-clause theory. The system has been tested in a variety of application domains, and results are presented for problems in the domains of molecular biology and plant disease diagnosis.

Connected through hyperedge HE-1 (induced by source paper "Automated Refinement of First-Order Horn-Clause Domain Theories"):
  with members:
    - "From Theory Refinement to KB Maintenance: a Position Statement"
    - "Revising Bayesian Network Parameters Using Backpropagation"
    - "References Automatic student modeling and bug library construction using theory refinement. Ph.D. ml/ Symbolic revision"
    - "LEARNING CONCEPTS BY ASKING QUESTIONS"

Connected through hyperedge HE-2 (induced by source paper "An Optimized Theory Revision Module"):
  with members:
    - "Theory Revision in Fault Hierarchies"
    - "Irrelevant Features and the Subset Selection Problem"
    - "The Complexity of Theory Revision"

Connected through hyperedge HE-3 (induced by source paper "Extending Theory Refinement to M-of-N Rules"):
  with members:
    - "Combining Connectionist and Symbolic Learning to Refine Certainty-Factor Rule Bases"
    - "ID2-of-3: Constructive Induction of M of-N Concepts for Discriminators in Decision Trees"

Connected through hyperedge HE-4 (induced by source paper "From Theory Refinement to KB Maintenance: a Position Statement"):
  with members:
    - "Automated Refinement of First-Order Horn-Clause Domain Theories"
    - "Multi-Strategy Learning and Theory Revision"

Connected through hyperedge HE-5 (induced by source paper "Finding Accurate Frontiers: A Knowledge-Intensive Approach to Relational Learning"):
  with members:
    - "Learning Analytically and Inductively"
    - "Covering vs. Divide-and-Conquer for Top-Down Induction of Logic Programs"
    - "Theory-Guided Induction of Logic Programs by Inference of Regular Languages recursive clauses. merlin on the"
    - "Specialization of Recursive Predicates"
    - "Specialization of Logic Programs by Pruning SLD-Trees"
    - "Knowledge Acquisition with a Knowledge-Intensive Machine Learning System"
    - "Generating Declarative Language Bias for Top-Down ILP Algorithms"

Connected through hyperedge HE-6 (induced by source paper "The Challenge of Revising an Impure Theory"):
  with members:
    - "Theory Revision in Fault Hierarchies"
    - "The Difficulties of Learning Logic Programs with Cut"
    - "The Complexity of Theory Revision"

Connected through hyperedge HE-7 (induced by source paper "Improving accuracy by combining rule-based and case-based reasoning"):
  with members:
    - "A Comparative Study of ID3 and Backpropagation for English Text-to-Speech Mapping"
    - "Language-Independent Data-Oriented Grapheme-to-Phoneme Conversion"
    - "Case-based Acquisition of User Preferences for Solution Improvement in Ill-Structured Domains"
    - "The evaluation of Anapron: A case study in evaluating a case-based system"
    - "A comparison of Anapron with seven other name-pronunciation systems"

Connected through hyperedge HE-8 (induced by source paper "Tractability of Theory Patching"):
  with members:
    - "Bias-Driven Revision of Logical Domain Theories"
    - "Multi-Strategy Learning and Theory Revision"

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