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: "Context-Based Similarity Applied to Retrieval of Relevant Cases"
  Abstract: Retrieving relevant cases is a crucial component of case-based reasoning systems. The task is to use user-defined query to retrieve useful information, i.e., exact matches or partial matches which are close to query-defined request according to certain measures. The difficulty stems from the fact that it may not be easy (or it may be even impossible) to specify query requests precisely and completely resulting in a situation known as a fuzzy-querying. It is usually not a problem for small domains, but for a large repositories which store various information (multifunctional information bases or a federated databases), a request specification becomes a bottleneck. Thus, a flexible retrieval algorithm is required, allowing for imprecise query specification and for changing the viewpoint. Efficient database techniques exists for locating exact matches. Finding relevant partial matches might be a problem. This document proposes a context-based similarity as a basis for flexible retrieval. Historical bacground on research in similarity assessment is presented and is used as a motivation for formal definition of context-based similarity. We also describe a similarity-based retrieval system for multifunctinal information bases.

Connected through hyperedge HE-1 (induced by source paper "How to Retrieve Relevant Information?"):
  with members:
    - "Constructive Similarity Assessment: Using Stored Cases to Define New Situations"
    - "A Similarity-Based Retrieval Tool for Software Repositories"

Connected through hyperedge HE-2 (induced by source paper "MAC/FAC: A Model of Similarity-based Retrieval"):
  with members:
    - "Structural Similarity as Guidance in Case-Based Design"
    - "Task-Oriented Knowledge Acquisition and Reasoning for Design Support Systems"
    - "The Structure-Mapping Engine: Algorithm and Examples"
    - "In  Estimating analogical similarity by dot-products of Holographic Reduced Representations."
    - "Proceedings of CogSci89 Structural Evaluation of Analogies: What Counts?"
    - "Making SME greedy and pragmatic"

Connected through hyperedge HE-3 (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"
    - "Case-Based Similarity Assessment: Estimating Adaptability from Experience"
    - "How to Retrieve Relevant Information?"

Connected through hyperedge HE-4 (induced by source paper "A Similarity-Based Retrieval Tool for Software Repositories"):
  with members:
    - "How to Retrieve Relevant Information?"
    - "A Case-Based Reasoning Approach"

Connected through hyperedge HE-5 (induced by source paper "Applying Case-Based Reasoning to Control in Robotics"):
  with members:
    - "A Case-Based Reasoning Approach"

Connected through hyperedge HE-6 (induced by source paper "INFERENTIAL THEORY OF LEARNING: Developing Foundations for Multistrategy Learning"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "A theory of questions and question asking"
    - "In Machine Learning: A Multistrategy Approach, Vol. IV  Macro and Micro Perspectives of Multistrategy Learning"
    - "The Use of Explicit Goals for Knowledge to Guide Inference and Learning"
    - "Issues in Goal-Driven Explanation"
    - "Hybrid Learning Using Genetic Algorithms and Decision Trees for Pattern Classification"
    - "Machine Learning and Inference"
    - "Machine Learning and Inference"

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