human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge groups papers that share a common citing paper.

Details of the hypergraph:
Center hyperedge (induced by source paper "Acquiring Recursive and Iterative Concepts with Explanation-Based Learning explanation-based generalization, generalizing explanation structures, generalizing to"):
  Abstract: University of Wisconsin Computer Sciences Technical Report 876 (September 1989) Abstract In explanation-based learning, a specific problem's solution is generalized into a form that can be later used to solve conceptually similar problems. Most research in explanation-based learning involves relaxing constraints on the variables in the explanation of a specific example, rather than generalizing the graphical structure of the explanation itself. However, this precludes the acquisition of concepts where an iterative or recursive process is implicitly represented in the explanation by a fixed number of applications. This paper presents an algorithm that generalizes explanation structures and reports empirical results that demonstrate the value of acquiring recursive and iterative concepts. The BAGGER2 algorithm learns recursive and iterative concepts, integrates results from multiple examples, and extracts useful subconcepts during generalization. On problems where learning a recursive rule is not appropriate, the system produces the same result as standard explanation-based methods. Applying the learned recursive rules only requires a minor extension to a PROLOG-like problem solver, namely, the ability to explicitly call a specific rule. Empirical studies demonstrate that generalizing the structure of explanations helps avoid the recently reported negative effects of learning.

Includes members:
  Member "LEARNING CONCEPTS BY ASKING QUESTIONS":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Inductive Logic Programming")
  Member "Hierarchical Explanation-Based Reinforcement Learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Transfer of Learning by Composing Solutions of Elemental Sequential Tasks")
  Member "Learning Goal-Decomposition Rules using Exercises":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Learning First-Order Acyclic Horn Programs from Entailment")
  Member "Theory-guided Empirical Speedup Learning of Goal Decomposition Rules":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "Combining FOIL and EBG to Speed-up Logic Programs")

Question: Please tell me which class the citing paper (i.e., the source paper of the center hyperedge) 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: Case_Based