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: "Bottom-up induction of logic programs with more than one recursive clause"
  Abstract: In this paper we present a bottom-up algorithm called MRI to induce logic programs from their examples. This method can induce programs with a base clause and more than one recursive clause from a very small number of examples. MRI is based on the analysis of saturations of examples. It first generates a path structure, which is an expression of a stream of values processed by predicates. The concept of path structure was originally introduced by Identam-Almquist and used in TIM [ Idestam-Almquist, 1996 ] . In this paper, we introduce the concepts of extension and difference of path structure. Recursive clauses can be expressed as a difference between a path structure and its extension. The paper presents the algorithm and shows experimental results obtained by the method.

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"
    - "Stochastic Propositionalization of Non-Determinate Background Knowledge"
    - "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"
    - "Relational Knowledge Discovery in Databases"

Connected through hyperedge HE-2 (induced by source paper "Inverting Implication with Small Training Sets"):
  with members:
    - "Learning Singly-Recursive Relations from Small Datasets"

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