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: "Learning by Refining Algorithm Sketches"
  Abstract: In this paper we suggest a mechanism that improves significantly the performance of a top-down inductive logic programming (ILP) learning system. This improvement is achieved at the cost of giving to the system extra information that is not difficult to formulate. This information appears in the form of an algorithm sketch: an incomplete and somewhat vague representation of the computation related to a particular example. We describe which sketches are admissible, give details of the learning algorithm that exploits the information contained in the sketch. The experiments carried out with the implemented system (SKIL) have demonstrated the usefulness of the method and its potential in future applications.

Connected through hyperedge HE-1 (induced by source paper "Architecture for Iterative Learning of Recursive Definitions"):
  with members:
    - "INFERENTIAL THEORY OF LEARNING: Developing Foundations for Multistrategy Learning"
    - "Integrity Constraints in ILP using a Monte Carlo approach"

Connected through hyperedge HE-2 (induced by source paper "Integrity Constraints in ILP using a Monte Carlo approach"):
  with members:
    - "Architecture for Iterative Learning of Recursive Definitions"

Connected through hyperedge HE-3 (induced by source paper "Learning Recursion with Iterative Bootstrap Induction (Extended Abstract)"):
  with members:
    - "INFERENTIAL THEORY OF LEARNING: Developing Foundations for Multistrategy 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