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: "ADAPtER: an Integrated Diagnostic System Combining Case-Based and Abductive Reasoning"
  Abstract: The aim of this paper is to describe the ADAPtER system, a diagnostic architecture combining case-based reasoning with abductive reasoning and exploiting the adaptation of the solution of old episodes, in order to focus the reasoning process. Domain knowledge is represented via a logical model and basic mechanisms, based on abductive reasoning with consistency constraints, have been defined for solving complex diagnostic problems involving multiple faults. The model-based component has been supplemented with a case memory and adaptation mechanisms have been developed, in order to make the diagnostic system able to exploit past experience in solving new cases. A heuristic function is proposed, able to rank the solutions associated to retrieved cases with respect to the adaptation effort needed to transform such solutions into possible solutions for the current case. We will discuss some preliminary experiments showing the validity of the above heuristic and the convenience of solving a new case by adapting a retrieved solution rather than solving the new problem from scratch.

Connected through hyperedge HE-1 (induced by source paper "A utility-based approach to learning in a mixed Case-Based and Model-Based Reasoning architecture"):
  with members:
    - "A Comparative Utility Analysis of Case-Based Reasoning and Control-Rule Learning Systems"
    - "On the Usefulness of Re-using Diagnostic Solutions"
    - "A Performance Model for Knowledge-based Systems"

Connected through hyperedge HE-2 (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"
    - "A utility-based approach to learning in a mixed Case-Based and Model-Based Reasoning architecture"

Connected through hyperedge HE-3 (induced by source paper "Focusing Construction and Selection of Abductive Hypotheses"):
  with members:
    - "Goal-based Explanation Evaluation 1"

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