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: "Theory-Guided Induction of Logic Programs by Inference of Regular Languages recursive clauses. merlin on the"
  Abstract: resent allowed sequences of resolution steps for the initial theory. There are, however, many characterizations of allowed sequences of resolution steps that cannot be expressed by a set of resolvents. One approach to this problem is presented, the system mer-lin, which is based on an earlier technique for learning finite-state automata that represent allowed sequences of resolution steps. merlin extends the previous technique in three ways: i) negative examples are considered in addition to positive examples, ii) a new strategy for performing generalization is used, and iii) a technique for converting the learned automaton to a logic program is included. Results from experiments are presented in which merlin outperforms both a system using the old strategy for performing generalization, and a traditional covering technique. The latter result can be explained by the limited expressiveness of hypotheses produced by covering and also by the fact that covering needs to produce the correct base clauses for a recursive definition before

Connected through hyperedge HE-1 (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"
    - "Specialization of Recursive Predicates"
    - "Specialization of Logic Programs by Pruning SLD-Trees"
    - "Knowledge Acquisition with a Knowledge-Intensive Machine Learning System"
    - "Generating Declarative Language Bias for Top-Down ILP Algorithms"

Connected through hyperedge HE-2 (induced by source paper "Predicate Invention and Learning from Positive Examples Only"):
  with members:
    (none)

Connected through hyperedge HE-3 (induced by source paper "Covering vs. Divide-and-Conquer for Top-Down Induction of Logic Programs"):
  with members:
    - "Finding Accurate Frontiers: A Knowledge-Intensive Approach to Relational Learning"
    - "Structural Regression Trees"
    - "Specialization of Recursive Predicates"
    - "Specialization of Logic Programs by Pruning SLD-Trees"

Connected through hyperedge HE-4 (induced by source paper "Specialization of Logic Programs by Pruning SLD-Trees"):
  with members:
    - "Finding Accurate Frontiers: A Knowledge-Intensive Approach to Relational Learning"
    - "Covering vs. Divide-and-Conquer for Top-Down Induction of Logic Programs"
    - "Specialization of Recursive Predicates"

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