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: "Abstract"
  Abstract: We describe an ongoing project to develop an adaptive training system (ATS) that dynamically models a students learning processes and can provide specialized tutoring adapted to a students knowledge state and learning style. The student modeling component of the ATS, ML-Modeler, uses machine learning (ML) techniques to emulate the students novice-to-expert transition. ML-Modeler infers which learning methods the student has used to reach the current knowledge state by comparing the students solution trace to an expert solution and generating plausible hypotheses about what misconceptions and errors the student has made. A case-based approach is used to generate hypotheses through incorrectly applying analogy, overgeneralization, and overspecialization. The student and expert models use a network-based representation that includes abstract concepts and relationships as well as strategies for problem solving. Fuzzy methods are used to represent the uncertainty in the student model. This paper describes the design of the ATS and ML-Modeler, and gives a detailed example of how the system would model and tutor the student in a typical session. The domain we use for this example is high-school level chemistry.

Connected through hyperedge HE-1 (induced by source paper "Representing Self-knowledge for Introspection about Memory Search"):
  with members:
    - "Abstract"
    - "Abstract"
    - "Learning to Improve Case Adaptation by Introspective Reasoning and CBR"

Connected through hyperedge HE-2 (induced by source paper "Modeling Case-based Planning for Repairing Reasoning Failures"):
  with members:
    - "Abstract"
    - "Abstract"
    - "Introspective reasoning using meta-explanations for multistrategy learning"
    - "Using Knowledge of Cognitive Behavior to Learn from Failure"
    - "Generic Teleological Mechanisms and their Use in Case Adaptation"
    - "Using Case-Based Reasoning for Mobile Robot Navigation"
    - "BECOMING AN EXPERT CASE-BASED REASONER: LEARNING TO ADAPT PRIOR CASES"

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