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: "The Origins of Inductive Logic Programming: A Prehistoric Tale"
  Abstract: This paper traces the development of the main ideas that have led to the present state of knowledge in Inductive Logic Programming. The story begins with research in psychology on the subject of human concept learning. Results from this research influenced early efforts in Artificial Intelligence which combined with the formal methods of inductive inference to evolve into the present discipline of Inductive Logic Programming. Inductive Logic Programming is often considered to be a young discipline. However, it has its roots in research dating back nearly 40 years. This paper traces the development of ideas beginning in psychology and the effect they had on concept learning research in Artificial Intelligence. Independent of any requirement for a psychological basis, formal methods of inductive inference were developed. These separate streams eventually gave rise to Inductive Logic Programming. This account is not entirely unbiased. More attention is given to the work of those researchers who most influenced my own interest in machine learning. Being a retrospective paper, I do not attempt to describe recent developments in ILP. This account only includes research prior to 1991 the year in which the term Inductive Logic Programming was first used (Muggleton, 1991). This is the reason for the subtitle A Prehistoric Tale. The major headings in the paper are taken from the names of periods in the evolution of life on Earth.

Connected through hyperedge HE-1 (induced by source paper "LEARNING CONCEPTS BY ASKING QUESTIONS"):
  with members:
    - "Inductive Logic Programming"
    - "Automated Refinement of First-Order Horn-Clause Domain Theories"
    - "Relating Relational Learning Algorithms"
    - "Acquiring Recursive and Iterative Concepts with Explanation-Based Learning explanation-based generalization, generalizing explanation structures, generalizing to"
    - "Explanations of Empirically Derived Reactive Plans"
    - "Observation and Generalisation in a Simulated Robot World"
    - "Learning First-Order Acyclic Horn Programs from Entailment"

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