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 "A utility-based approach to learning in a mixed Case-Based and Model-Based Reasoning architecture"):
  Abstract: Case-based reasoning (CBR) can be used as a form of "caching" solved problems to speedup later problem solving. Using "cached" cases brings additional costs with it due to retrieval time, case adaptation time and also storage space. Simply storing all cases will result in a situation in which retrieving and trying to adapt old cases will take more time (on average) than not caching at all. This means that caching must be applied selectively to build a case memory that is actually useful. This is a form of the utility problem [4, 2]. The approach taken here is to construct a "cost model" of a system that can be used to predict the effect of changes to the system. In this paper we describe the utility problem associated with "caching" cases and the construction of a "cost model". We present experimental results that demonstrate that the model can be used to predict the effect of certain changes to the case memory.

Includes members:
  Member "A Comparative Utility Analysis of Case-Based Reasoning and Control-Rule Learning Systems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "An Explanation-Based Approach to Improve Retrieval in Case-Based Planning")
  Member "ADAPtER: an Integrated Diagnostic System Combining Case-Based and Abductive Reasoning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "On the Usefulness of Re-using Diagnostic Solutions")
  Member "On the Usefulness of Re-using Diagnostic Solutions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "ADAPtER: an Integrated Diagnostic System Combining Case-Based and Abductive Reasoning")
  Member "A Performance Model for Knowledge-based Systems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "Redesigning control knowledge of knowledge-based systems: machine learning meets knowledge engineering")

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