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: "Case-Based Similarity Assessment: Estimating Adaptability from Experience"
  Abstract: Case-based problem-solving systems rely on similarity assessment to select stored cases whose solutions are easily adaptable to fit current problems. However, widely-used similarity assessment strategies, such as evaluation of semantic similarity, can be poor predictors of adaptability. As a result, systems may select cases that are difficult or impossible for them to adapt, even when easily adaptable cases are available in memory. This paper presents a new similarity assessment approach which couples similarity judgments directly to a case library containing the system's adaptation knowledge. It examines this approach in the context of a case-based planning system that learns both new plans and new adaptations. Empirical tests of alternative similarity assessment strategies show that this approach enables better case selection and increases the benefits accrued from learned adaptations.

Connected through hyperedge HE-1 (induced by source paper "A Case Study of Case-Based CBR"):
  with members:
    - "Acquiring Case Adaptation Knowledge: A Hybrid Approach"
    - "Supporting Combined Human and Machine Planning: An Interface for Planning by Analogical Reasoning"
    - "Learning to Integrate Multiple Knowledge Sources for Case-Based Reasoning"
    - "Case-Based Learning: Beyond Classification of Feature Vectors"

Connected through hyperedge HE-2 (induced by source paper "Constructive Similarity Assessment: Using Stored Cases to Define New Situations"):
  with members:
    - "Rules and Precedents as Complementary Warrants Complementarity of Rules and Precedents for Classification In a"
    - "Learning to Integrate Multiple Knowledge Sources for Case-Based Reasoning"
    - "How to Retrieve Relevant Information?"
    - "Context-Based Similarity Applied to Retrieval of Relevant Cases"

Connected through hyperedge HE-3 (induced by source paper "Learning to Integrate Multiple Knowledge Sources for Case-Based Reasoning"):
  with members:
    - "Constructive Similarity Assessment: Using Stored Cases to Define New Situations"
    - "In Machine Learning: A Multistrategy Approach, Vol. IV  Macro and Micro Perspectives of Multistrategy Learning"
    - "Acquiring Case Adaptation Knowledge: A Hybrid Approach"
    - "Supporting Combined Human and Machine Planning: An Interface for Planning by Analogical Reasoning"
    - "A Case Study of Case-Based CBR"

Connected through hyperedge HE-4 (induced by source paper "Acquiring Case Adaptation Knowledge: A Hybrid Approach"):
  with members:
    - "Learning to Improve Case Adaptation by Introspective Reasoning and CBR"
    - "Combining Rules and Cases to Learn Case Adaptation"
    - "Towards A Computer Model of Memory Search Strategy Learning"
    - "Learning to Integrate Multiple Knowledge Sources for Case-Based Reasoning"
    - "A Case Study of Case-Based CBR"
    - "on Case-Based Reasoning Integrations Case-Based Seeding for an Interactive Crisis Response Assistant"

Connected through hyperedge HE-5 (induced by source paper "Learning to Refine Indexing by Introspective Reasoning"):
  with members:
    (none)

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