human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge groups papers that share a common citing paper.

Details of the hypergraph:
Center hyperedge (induced by source paper "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

Includes members:
  Member "21 Using n 2 classifier in constructive induction":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley.")
  Member "INFERENTIAL THEORY OF LEARNING: Developing Foundations for Multistrategy Learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Context-Based Similarity Applied to Retrieval of Relevant Cases")
  Member "How to Get a Free Lunch: A Simple Cost Model for Machine Learning Applications":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Cost-Sensitive Classification: Empirical Evaluation of a Hybrid Genetic Decision Tree Induction Algorithm")
  Member "Learning to Integrate Multiple Knowledge Sources for Case-Based Reasoning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "A Case Study of Case-Based CBR")

Question: Please tell me which class the citing paper (i.e., the source paper of the center hyperedge) 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