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: "Dlab: A Declarative Language Bias Formalism"
  Abstract: We describe the principles and functionalities of Dlab (Declarative LAnguage Bias). Dlab can be used in inductive learning systems to define syntactically and traverse efficiently finite subspaces of first order clausal logic, be it a set of propositional formulae, association rules, Horn clauses, or full clauses. A Prolog implementation of Dlab is available by ftp access. Keywords: declarative language bias, concept learning, knowledge dis covery

Connected through hyperedge HE-1 (induced by source paper "Evaluation and Selection of Biases in Machine Learning"):
  with members:
    - "Irrelevant Features and the Subset Selection Problem"
    - "Evolution-based Discovery of Hierarchical Behaviors"
    - "#1 Robust Feature Selection Algorithms"
    - "Improving Tactical Plans with Genetic Algorithms"
    - "Model selection using measure functions"
    - "Utilizing Prior Concepts for Learning"

Connected through hyperedge HE-2 (induced by source paper "Inductive Database Design"):
  with members:
    - "Applications of a logical discovery engine"
    - "Learning with Abduction"

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