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: "Inverse entailment and Progol"
  Abstract: This paper firstly provides a re-appraisal of the development of techniques for inverting deduction, secondly introduces Mode-Directed Inverse Entailment (MDIE) as a generalisation and enhancement of previous approaches and thirdly describes an implementation of MDIE in the Progol system. Progol is implemented in C and available by anonymous ftp. The re-assessment of previous techniques in terms of inverse entailment leads to new results for learning from positive data and inverting implication between pairs of clauses.

Connected through hyperedge HE-1 (induced by source paper "Predicting Ordinal Classes in ILP"):
  with members:
    - "Cost-Sensitive Classification: Empirical Evaluation of a Hybrid Genetic Decision Tree Induction Algorithm"
    - "Fossil: A Robust Relational Learner"

Connected through hyperedge HE-2 (induced by source paper "Stochastic Propositionalization of Non-Determinate Background Knowledge"):
  with members:
    - "Theory and Applications of Agnostic PAC-Learning with Small Decision Trees"
    - "Learning Trees and Rules with Set-valued Features"
    - "SFOIL: Stochastic Approach to Inductive Logic Programming"

Connected through hyperedge HE-3 (induced by source paper "Bottom-up induction of logic programs with more than one recursive clause"):
  with members:
    - "Inverting Implication with Small Training Sets"

Connected through hyperedge HE-4 (induced by source paper "Relational Knowledge Discovery in Databases"):
  with members:
    - "Inductive Constraint Logic"

Connected through hyperedge HE-5 (induced by source paper "Mining for Causes of Cancer: Machine Learning Experiments at Various Levels of Detail"):
  with members:
    - "Theory and Applications of Agnostic PAC-Learning with Small Decision Trees"
    - "Generating Declarative Language Bias for Top-Down ILP Algorithms"

Connected through hyperedge HE-6 (induced by source paper "Extraction of Meta-Knowledge to Restrict the Hypothesis Space for ILP Systems incorporating them in FOIL."):
  with members:
    - "ILP with Noise and Fixed Example Size: A Bayesian Approach"

Connected through hyperedge HE-7 (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"
    - "Stochastic Propositionalization of Non-Determinate Background Knowledge"

Connected through hyperedge HE-8 (induced by source paper "Which Hypotheses Can Be Found with Inverse Entailment? -Extended Abstract"):
  with members:
    - "Pac-Learning Recursive Logic Programs: Efficient Algorithms"

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