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: "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.

Connected through hyperedge HE-1 (induced by source paper "Inverse entailment and Progol"):
  with members:
    - "Learning the Past Tense of English Verbs: The Symbolic Pattern Associator vs. Connectionist Models"
    - "Predicting Ordinal Classes in ILP"
    - "Learning Trees and Rules with Set-valued Features"
    - "Mining for Causes of Cancer: Machine Learning Experiments at Various Levels of Detail"
    - "Extraction of Meta-Knowledge to Restrict the Hypothesis Space for ILP Systems incorporating them in FOIL."
    - "Which Hypotheses Can Be Found with Inverse Entailment? -Extended Abstract"
    - "Bottom-up induction of logic programs with more than one recursive clause"
    - "Relational Knowledge Discovery in Databases"

Connected through hyperedge HE-2 (induced by source paper "SFOIL: Stochastic Approach to Inductive Logic Programming"):
  with members:
    - "Stochastic search in inductive concept learning"
    - "Naive Bayesian classifier within ILP-R"
    - "Linear Space Induction in First Order Logic with RELIEFF"
    - "An application of ILP in a musical database: learning to compose the two-voice counterpoint"
    - "Overcoming the myopia of inductive learning algorithms with RELIEFF"

Connected through hyperedge HE-3 (induced by source paper "Learning Trees and Rules with Set-valued Features"):
  with members:
    - "Heterogeneous Uncertainty Sampling for Supervised Learning"
    - "Context-sensitive learning methods for text categorization"
    - "Transferring and Retraining Learned Information Filters"
    - "Inverse entailment and Progol"

Connected through hyperedge HE-4 (induced by source paper "Theory and Applications of Agnostic PAC-Learning with Small Decision Trees"):
  with members:
    - "Pessimistic decision tree pruning based on tree size"
    - "Learning Active Classifiers"
    - "Error-Based and Entropy-Based Discretization of Continuous Features"
    - "Mining for Causes of Cancer: Machine Learning Experiments at Various Levels of Detail"

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