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 Control Knowledge in Models of Expertise ECML'95 Workshop on Knowledge-Level Modelling and Machine Learning"
  Abstract: During the development and the life-cycle of knowledge-based systems the requirements on the system and the knowledge in the system will change. One of the types of knowledge affected by changing requirements is control-knowledge, which prescribes the ordering of problem-solving steps. Machine-learning can aid developers of knowledge-based systems in adapting their systems to changing requirements. A number of machine-learning techniques for learning control-knowledge have been applied to problem-solvers (Prodigy-EBL, LEX). In knowledge engineering, the focus has shifted to the construction of knowledge-level models of problem-solving instead of directly constructing a knowledge-based system in a problem-solver. In this paper we describe work in progress on how to apply machine learning techniques to the KADS model of expertise.

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"
    - "Redesigning control knowledge of knowledge-based systems: machine learning meets knowledge engineering"

Connected through hyperedge HE-2 (induced by source paper "Problem Solving for Redesign"):
  with members:
    (none)

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