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: "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.

Connected through hyperedge HE-1 (induced by source paper "Hierarchical Explanation-Based Reinforcement Learning"):
  with members:
    - "Transfer of Learning by Composing Solutions of Elemental Sequential Tasks"
    - "Machine Learning,  Explanation-Based Learning and Reinforcement Learning: A Unified View"

Connected through hyperedge HE-2 (induced by source paper "LEARNING CONCEPTS BY ASKING QUESTIONS"):
  with members:
    - "Inductive Logic Programming"
    - "Automated Refinement of First-Order Horn-Clause Domain Theories"
    - "Relating Relational Learning Algorithms"
    - "Explanations of Empirically Derived Reactive Plans"
    - "Observation and Generalisation in a Simulated Robot World"
    - "Learning First-Order Acyclic Horn Programs from Entailment"
    - "The Origins of Inductive Logic Programming: A Prehistoric Tale"

Connected through hyperedge HE-3 (induced by source paper "Theory-guided Empirical Speedup Learning of Goal Decomposition Rules"):
  with members:
    - "Learning Goal-Decomposition Rules using Exercises"
    - "Combining FOIL and EBG to Speed-up Logic Programs"

Connected through hyperedge HE-4 (induced by source paper "Learning Goal-Decomposition Rules using Exercises"):
  with members:
    - "Theory-guided Empirical Speedup Learning of Goal Decomposition Rules"
    - "Combining FOIL and EBG to Speed-up Logic Programs"
    - "Learning First-Order Acyclic Horn Programs from Entailment"
    - "Learning Horn Definitions with Equivalence and Membership Queries"

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: Case_Based