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: "Specialization of Logic Programs by Pruning SLD-Trees"
  Abstract: program w.r.t. positive and negative examples can be viewed as the problem of pruning an SLD-tree such that all refutations of negative examples and no refutations of positive examples are excluded. It is shown that the actual pruning can be performed by applying unfolding and clause removal. The algorithm spectre is presented, which is based on this idea. The input to the algorithm is, besides a logic program and positive and negative examples, a computation rule, which determines the shape of the SLD-tree that is to be pruned. It is shown that the generality of the resulting specialization is dependent on the computation rule, and experimental results are presented from using three different computation rules. The experiments indicate that the computation rule should be formulated so that the number of applications of unfolding is kept as low as possible. The algorithm, which uses a divide-and-conquer method, is also compared with a covering algorithm. The experiments show that a higher predictive accuracy can be achieved if the focus is on discriminating positive from negative examples rather than on achieving a high coverage of positive examples only.

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"
    - "Theory-Guided Induction of Logic Programs by Inference of Regular Languages recursive clauses. merlin on the"
    - "Specialization of Recursive Predicates"
    - "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 "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"
    - "Theory-Guided Induction of Logic Programs by Inference of Regular Languages recursive clauses. merlin on the"
    - "Specialization of Recursive Predicates"

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

Connected through hyperedge HE-4 (induced by source paper "Theory-Guided Induction of Logic Programs by Inference of Regular Languages recursive clauses. merlin on the"):
  with members:
    - "Finding Accurate Frontiers: A Knowledge-Intensive Approach to Relational Learning"
    - "Covering vs. Divide-and-Conquer for Top-Down Induction of Logic Programs"
    - "Predicate Invention and Learning from Positive Examples Only"

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