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: "In Machine Learning: A Multistrategy Approach, Vol. IV  Macro and Micro Perspectives of Multistrategy Learning"
  Abstract: Machine learning techniques are perceived to have a great potential as means for the acquisition of knowledge; nevertheless, their use in complex engineering domains is still rare. Most machine learning techniques have been studied in the context of knowledge acquisition for well defined tasks, such as classification. Learning for these tasks can be handled by relatively simple algorithms. Complex domains present difficulties that can be approached by combining the strengths of several complementing learning techniques, and overcoming their weaknesses by providing alternative learning strategies. This study presents two perspectives, the macro and the micro, for viewing the issue of multistrategy learning. The macro perspective deals with the decomposition of an overall complex learning task into relatively well-defined learning tasks, and the micro perspective deals with designing multistrategy learning techniques for supporting the acquisition of knowledge for each task. The two perspectives are discussed in the context of

Connected through hyperedge HE-1 (induced by source paper "21 Using n 2 classifier in constructive induction"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "Irrelevant Features and the Subset Selection Problem"
    - "Classification by Pairwise Coupling"

Connected through hyperedge HE-2 (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"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "How to Get a Free Lunch: A Simple Cost Model for Machine Learning Applications"):
  with members:
    - "Cost-Sensitive Classification: Empirical Evaluation of a Hybrid Genetic Decision Tree Induction Algorithm"
    - "Appears in Working Notes, Integrating Multiple Learned Models for Improving and Scaling Machine Learning Algorithms"

Connected through hyperedge HE-4 (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"
    - "The Use of Explicit Goals for Knowledge to Guide Inference and Learning"
    - "Issues in Goal-Driven Explanation"
    - "Context-Based Similarity Applied to Retrieval of Relevant Cases"
    - "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