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: "On the Usefulness of Re-using Diagnostic Solutions"
  Abstract: Recent studies on planning, comparing plan re-use and plan generation, have shown that both the above tasks may have the same degree of computational complexity, even if we deal with very similar problems. The aim of this paper is to show that the same kind of results apply also for diagnosis. We propose a theoretical complexity analysis coupled with some experimental tests, intended to evaluate the adequacy of adaptation strategies which re-use the solutions of past diagnostic problems in order to build a solution to the problem to be solved. Results of such analysis show that, even if diagnosis re-use falls into the same complexity class of diagnosis generation (they are both NP-complete problems), practical advantages can be obtained by exploiting a hybrid architecture combining case-based and model-based diagnostic problem solving in a unifying framework.

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"
    - "ADAPtER: an Integrated Diagnostic System Combining Case-Based and Abductive Reasoning"
    - "A Performance Model for Knowledge-based Systems"

Connected through hyperedge HE-2 (induced by source paper "ADAPtER: an Integrated Diagnostic System Combining Case-Based and Abductive Reasoning"):
  with members:
    - "A utility-based approach to learning in a mixed Case-Based and Model-Based Reasoning architecture"
    - "Focusing Construction and Selection of Abductive Hypotheses"

Connected through hyperedge HE-3 (induced by source paper "Supporting Combined Human and Machine Planning: An Interface for Planning by Analogical Reasoning"):
  with members:
    - "S o l u t i o n Relevant A b s t r a"
    - "Learning to Improve Case Adaptation by Introspective Reasoning and CBR"
    - "Learning to Integrate Multiple Knowledge Sources for Case-Based Reasoning"
    - "A Case Study of Case-Based CBR"
    - "Merge Strategies for Multiple Case Plan Replay"
    - "Supporting Combined Human and Machine Planning: The Prodigy 4.0 User Interface Version 2.0*"
    - "Towards Mixed-Initiative Rationale-Supported Planning"

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