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: "Generating Declarative Language Bias for Top-Down ILP Algorithms"
  Abstract: Many of today's algorithms for Inductive Logic Programming (ILP) put a heavy burden and responsibility on the user, because their declarative bias have to be defined in a rather low-level fashion. To address this issue, we developed a method for generating declarative language bias for top-down ILP systems from high-level declarations. The key feature of our approach is the distinction between a user level and an expert level of language bias declarations. The expert provides abstract meta-declarations, and the user declares the relationship between the meta-level and the given database to obtain a low-level declarative language bias. The suggested languages allow for compact and abstract specifications of the declarative language bias for top-down ILP systems using schemata. We verified several properties of the translation algorithm that generates schemata, and applied it successfully to a few chemical domains. As a consequence, we propose to use a two-level approach to generate declarative language bias.

Connected through hyperedge HE-1 (induced by source paper "A Comparison of Pruning Methods for Relational Concept Learning"):
  with members:
    - "Mingers, 1989 J. Mingers. An empirical comparison of pruning methods for decision tree induction. Machine"
    - "An investigation of noise-tolerant relational concept learning algorithms"
    - "Fossil: A Robust Relational Learner"
    - "Top-Down Pruning in Relational Learning"

Connected through hyperedge HE-2 (induced by source paper "Top-down Induction of Logical Decision Trees"):
  with members:
    - "Lookahead and Discretization in ILP"

Connected through hyperedge HE-3 (induced by source paper "Finding Accurate Frontiers: A Knowledge-Intensive Approach to Relational Learning"):
  with members:
    - "Learning Analytically and Inductively"
    - "Theory Refinement Combining Analytical and Empirical Methods"
    - "Covering vs. Divide-and-Conquer for Top-Down Induction of Logic Programs"
    - "Theory-Guided Induction of Logic Programs by Inference of Regular Languages recursive clauses. merlin on the"
    - "Specialization of Recursive Predicates"
    - "Specialization of Logic Programs by Pruning SLD-Trees"
    - "Knowledge Acquisition with a Knowledge-Intensive Machine Learning System"

Connected through hyperedge HE-4 (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"
    - "Inverse entailment and Progol"

Connected through hyperedge HE-5 (induced by source paper "Applying ILP to Diterpene Structure Elucidation from 13 C NMR Spectra"):
  with members:
    - "Rule Induction with CN2: Some Recent Improvements"
    - "An intelligent search method using Inductive Logic Programming"
    - "Inductive Constraint Logic"
    - "Lookahead and Discretization in ILP"

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