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: "Finding Accurate Frontiers: A Knowledge-Intensive Approach to Relational Learning"
  Abstract: An approach to analytic learning is described that searches for accurate entailments of a Horn Clause domain theory. A hill-climbing search, guided by an information based evaluation function, is performed by applying a set of operators that derive frontiers from domain theories. The analytic learning system is one component of a multi-strategy relational learning system. We compare the accuracy of concepts learned with this analytic strategy to concepts learned with an analytic strategy that operationalizes the domain theory.

Connected through hyperedge HE-1 (induced by source paper "Generating Declarative Language Bias for Top-Down ILP Algorithms"):
  with members:
    - "A Comparison of Pruning Methods for Relational Concept 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-2 (induced by source paper "Covering vs. Divide-and-Conquer for Top-Down Induction of Logic Programs"):
  with members:
    - "Structural Regression Trees"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "Specialization of Recursive Predicates"):
  with members:
    - "Covering vs. Divide-and-Conquer for Top-Down Induction of Logic Programs"
    - "Specialization of Logic Programs by Pruning SLD-Trees"

Connected through hyperedge HE-4 (induced by source paper "Specialization of Logic Programs by Pruning SLD-Trees"):
  with members:
    - "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"

Connected through hyperedge HE-5 (induced by source paper "Theory-Guided Induction of Logic Programs by Inference of Regular Languages recursive clauses. merlin on the"):
  with members:
    - "Covering vs. Divide-and-Conquer for Top-Down Induction of Logic Programs"
    - "Specialization of Logic Programs by Pruning SLD-Trees"
    - "Predicate Invention and Learning from Positive Examples Only"

Connected through hyperedge HE-6 (induced by source paper "Knowledge Acquisition with a Knowledge-Intensive Machine Learning System"):
  with members:
    (none)

Connected through hyperedge HE-7 (induced by source paper "Theory Refinement Combining Analytical and Empirical Methods"):
  with members:
    - "An Optimized Theory Revision Module"
    - "The Challenge of Revising an Impure Theory"
    - "Learning Analytically and Inductively"
    - "From Theory Refinement to KB Maintenance: a Position Statement"
    - "Tractability of Theory Patching"
    - "Revising Bayesian Network Parameters Using Backpropagation"
    - "Combining Connectionist and Symbolic Learning to Refine Certainty-Factor Rule Bases"
    - "Extending Theory Refinement to M-of-N Rules"

Connected through hyperedge HE-8 (induced by source paper "Learning Analytically and Inductively"):
  with members:
    - "Learning to Act using Real-Time Dynamic Programming"
    - "Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,"
    - "Integrating Initialization Bias and Search Bias in Neural Network Learning"
    - "Theory Refinement Combining Analytical and Empirical Methods"

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