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: "Constructive Similarity Assessment: Using Stored Cases to Define New Situations"
  Abstract: A fundamental issue in case-based reasoning is similarity assessment: determining similarities and differences between new and retrieved cases. Many methods have been developed for comparing input case descriptions to the cases already in memory. However, the success of such methods depends on the input case description being sufficiently complete to reflect the important features of the new situation, which is not assured. In case-based explanation of anomalous events during story understanding, the anomaly arises because the current situation is incompletely understood; consequently, similarity assessment based on matches between known current features and old cases is likely to fail because of gaps in the current case's description. Our solution to the problem of gaps in a new case's description is an approach that we call constructive similarity assessment. Constructive similarity assessment treats similarity assessment not as a simple comparison between fixed new and old cases, but as a process for deciding which types of features should be investigated in the new situation and, if the features are borne out by other knowledge, added to the description of the current case. Constructive similarity assessment does not merely compare new cases to old: using prior cases as its guide, it dynamically carves augmented descriptions of new cases out of memory.

Connected through hyperedge HE-1 (induced by source paper "Context-Based Similarity Applied to Retrieval of Relevant Cases"):
  with members:
    - "INFERENTIAL THEORY OF LEARNING: Developing Foundations for Multistrategy Learning"
    - "MAC/FAC: A Model of Similarity-based Retrieval"
    - "How to Retrieve Relevant Information?"
    - "A Similarity-Based Retrieval Tool for Software Repositories"
    - "Applying Case-Based Reasoning to Control in Robotics"

Connected through hyperedge HE-2 (induced by source paper "How to Retrieve Relevant Information?"):
  with members:
    - "Context-Based Similarity Applied to Retrieval of Relevant Cases"
    - "A Similarity-Based Retrieval Tool for Software Repositories"

Connected through hyperedge HE-3 (induced by source paper "Case-Based Similarity Assessment: Estimating Adaptability from Experience"):
  with members:
    - "Acquiring Case Adaptation Knowledge: A Hybrid Approach"
    - "Learning to Integrate Multiple Knowledge Sources for Case-Based Reasoning"
    - "A Case Study of Case-Based CBR"
    - "Learning to Refine Indexing by Introspective Reasoning"

Connected through hyperedge HE-4 (induced by source paper "Rules and Precedents as Complementary Warrants Complementarity of Rules and Precedents for Classification In a"):
  with members:
    - "A Computational Model of Ratio Decidendi"

Connected through hyperedge HE-5 (induced by source paper "Learning to Integrate Multiple Knowledge Sources for Case-Based Reasoning"):
  with members:
    - "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"
    - "Case-Based Similarity Assessment: Estimating Adaptability from Experience"
    - "A Case Study of Case-Based CBR"

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