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 intelligent search method using Inductive Logic Programming"
  Abstract: We propose a method to use Inductive Logic Programming to give heuristic functions for searching goals to solve problems. The method takes solutions of a problem or a history of search and a set of background knowledge on the problem. In a large class of problems, a problem is described as a set of states and a set of operators, and is solved by finding a series of operators. A solution, a series of operators that brings an initial state to a final state, is transformed into positive and negative examples of a relation "better-choice", which describes that an operator is better than others in a state. We also give a way to use the "better-choice" relation as a heuristic function. The method can use any logic program as background knowledge to induce heuristics, and induced heuristics has high readability. The paper inspects the method by applying to a puzzle.

Connected through hyperedge HE-1 (induced by source paper "Applying ILP to Diterpene Structure Elucidation from 13 C NMR Spectra"):
  with members:
    - "Rule Induction with CN2: Some Recent Improvements"
    - "Inductive Constraint Logic"
    - "Generating Declarative Language Bias for Top-Down ILP Algorithms"
    - "Lookahead and Discretization in ILP"

Connected through hyperedge HE-2 (induced by source paper "Combining FOIL and EBG to Speed-up Logic Programs"):
  with members:
    - "Learning Semantic Grammars with Constructive Inductive Logic Programming"
    - "Learning Goal-Decomposition Rules using Exercises"
    - "Theory-guided Empirical Speedup Learning of Goal Decomposition Rules"
    - "Learning Approximate Control Rules Of High Utility"
    - "Learning First-Order Definitions of Functions"

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