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: "Learning to Integrate Multiple Knowledge Sources for Case-Based Reasoning"
  Abstract: The case-based reasoning process depends on multiple overlapping knowledge sources, each of which provides an opportunity for learning. Exploiting these opportunities requires not only determining the learning mechanisms to use for each individual knowledge source, but also how the different learning mechanisms interact and their combined utility. This paper presents a case study examining the relative contributions and costs involved in learning processes for three different knowledge sources|cases, case adaptation knowledge, and similarity information|in a case-based planner. It demonstrates the importance of interactions between different learning processes and identifies a promising method for integrating multiple learning methods to improve case-based reasoning.

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"
    - "Case-Based Similarity Assessment: Estimating Adaptability from Experience"
    - "Case-Based Learning: Beyond Classification of Feature Vectors"

Connected through hyperedge HE-2 (induced by source paper "In Machine Learning: A Multistrategy Approach, Vol. IV  Macro and Micro Perspectives of Multistrategy Learning"):
  with members:
    - "21 Using n 2 classifier in constructive induction"
    - "INFERENTIAL THEORY OF LEARNING: Developing Foundations for Multistrategy Learning"
    - "How to Get a Free Lunch: A Simple Cost Model for Machine Learning Applications"

Connected through hyperedge HE-3 (induced by source paper "Case-Based Similarity Assessment: Estimating Adaptability from Experience"):
  with members:
    - "Constructive Similarity Assessment: Using Stored Cases to Define New Situations"
    - "Acquiring Case Adaptation Knowledge: A Hybrid Approach"
    - "A Case Study of Case-Based CBR"
    - "Learning to Refine Indexing by Introspective Reasoning"

Connected through hyperedge HE-4 (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"
    - "Case-Based Similarity Assessment: Estimating Adaptability from Experience"
    - "How to Retrieve Relevant Information?"
    - "Context-Based Similarity Applied to Retrieval of Relevant Cases"

Connected through hyperedge HE-5 (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"
    - "On the Usefulness of Re-using Diagnostic Solutions"
    - "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"

Connected through hyperedge HE-6 (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"
    - "Case-Based Similarity Assessment: Estimating Adaptability from Experience"
    - "A Case Study of Case-Based CBR"
    - "on Case-Based Reasoning Integrations Case-Based Seeding for an Interactive Crisis Response Assistant"

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