human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge groups papers that share a common citing paper.

Details of the hypergraph:
Center hyperedge (induced by source paper "Stochastic Propositionalization of Non-Determinate Background Knowledge"):
  Abstract: It is a well-known fact that propositional learning algorithms require "good" features to perform well in practice. So a major step in data engineering for inductive learning is the construction of good features by domain experts. These features often represent properties of structured objects, where a property typically is the occurrence of a certain substructure having certain properties. To partly automate the process of "feature engineering", we devised an algorithm that searches for features which are defined by such substructures. The algorithm stochastically conducts a top-down search for first-order clauses, where each clause represents a binary feature. It differs from existing algorithms in that its search is not class-blind, and that it is capable of considering clauses ("context") of almost arbitrary length (size). Preliminary experiments are favorable, and support the view that this approach is promising.

Includes members:
  Member "Theory and Applications of Agnostic PAC-Learning with Small Decision Trees":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Learning Active Classifiers")
  Member "Learning Trees and Rules with Set-valued Features":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Inverse entailment and Progol")
  Member "SFOIL: Stochastic Approach to Inductive Logic Programming":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Overcoming the myopia of inductive learning algorithms with RELIEFF")
  Member "Inverse entailment and Progol":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "Predicting Ordinal Classes in ILP")

Question: Please tell me which class the citing paper (i.e., the source paper of the center hyperedge) 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