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 Problem-Solving Concepts by Reflecting on Problem Solving"
  Abstract: Learning and problem solving are intimately related: problem solving determines the knowledge requirements of the reasoner which learning must fulfill, and learning enables improved problem-solving performance. Different models of problem solving, however, recognize different knowledge needs, and, as a result, set up different learning tasks. Some recent models analyze problem solving in terms of generic tasks, methods, and subtasks. These models require the learning of problem-solving concepts such as new tasks and new task decompositions. We view reflection as a core process for learning these problem-solving concepts. In this paper, we identify the learning issues raised by the task-structure framework of problem solving. We view the problem solver as an abstract device, and represent how it works in terms of a structure-behavior-function model which specifies how the knowledge and reasoning of the problem solver results in the accomplishment of its tasks. We describe how this model enables reflection, and how model-based reflection enables the reasoner to adapt its task structure to produce solutions of better quality. The Autognostic system illustrates this reflection process.

Connected through hyperedge HE-1 (induced by source paper "Introspective reasoning using meta-explanations for multistrategy learning"):
  with members:
    - "Modeling Case-based Planning for Repairing Reasoning Failures"
    - "Abstract"
    - "Integrating Creativity and Reading: A Functional Approach"
    - "Role of Ontology in Creative Understanding"
    - "Abstract"
    - "A Functional Theory of Creative Reading"
    - "Towards A Computer Model of Memory Search Strategy Learning"
    - "Issues in Goal-Driven Explanation"

Connected through hyperedge HE-2 (induced by source paper "Redesigning control knowledge of knowledge-based systems: machine learning meets knowledge engineering"):
  with members:
    - "A Performance Model for Knowledge-based Systems"

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