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: "Redesigning control knowledge of knowledge-based systems: machine learning meets knowledge engineering"
  Abstract: Machine learning and knowledge engineering have always been strongly related, but the introduction of new representations in knowledge engineering has created a gap between them. This paper describes research aimed at applying machine learning techniques to the current knowledge engineering representations. We propose a system that redesigns a part of a knowledge based system, the so called control knowledge. We claim a strong similarity between redesign of knowledge based systems and incremental machine learning. Finally we will relate this work to existing research.

Connected through hyperedge HE-1 (induced by source paper "A Performance Model for Knowledge-based Systems"):
  with members:
    - "A utility-based approach to learning in a mixed Case-Based and Model-Based Reasoning architecture"
    - "Learning Control Knowledge in Models of Expertise ECML'95 Workshop on Knowledge-Level Modelling and Machine Learning"

Connected through hyperedge HE-2 (induced by source paper "Learning Problem-Solving Concepts by Reflecting on Problem Solving"):
  with members:
    - "Introspective reasoning using meta-explanations for multistrategy learning"

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