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: "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.

Connected through hyperedge HE-1 (induced by source paper "A Comparative Utility Analysis of Case-Based Reasoning and Control-Rule Learning Systems"):
  with members:
    - "AN EMPIRICAL APPROACH TO SOLVING THE GENERAL UTILITY PROBLEM IN SPEEDUP LEARNING"
    - "The Use of Explicit Goals for Knowledge to Guide Inference and Learning"
    - "Design and Implementation of a Replay Framework based on a Partial Order Planner"
    - "An Explanation-Based Approach to Improve Retrieval in Case-Based Planning"

Connected through hyperedge HE-2 (induced by source paper "A Performance Model for Knowledge-based Systems"):
  with members:
    - "Redesigning control knowledge of knowledge-based systems: machine learning meets knowledge engineering"
    - "Learning Control Knowledge in Models of Expertise ECML'95 Workshop on Knowledge-Level Modelling and Machine Learning"

Connected through hyperedge HE-3 (induced by source paper "ADAPtER: an Integrated Diagnostic System Combining Case-Based and Abductive Reasoning"):
  with members:
    - "On the Usefulness of Re-using Diagnostic Solutions"
    - "Focusing Construction and Selection of Abductive Hypotheses"

Connected through hyperedge HE-4 (induced by source paper "On the Usefulness of Re-using Diagnostic Solutions"):
  with members:
    - "Supporting Combined Human and Machine Planning: An Interface for Planning by Analogical Reasoning"
    - "ADAPtER: an Integrated Diagnostic System Combining Case-Based and Abductive Reasoning"

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