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: "Issues in Goal-Driven Explanation"
  Abstract: When a reasoner explains surprising events for its internal use, a key motivation for explaining is to perform learning that will facilitate the achievement of its goals. Human explainers use a range of strategies to build explanations, including both internal reasoning and external information search, and goal-based considerations have a profound effect on their choices of when and how to pursue explanations. However, standard AI models of explanation rely on goal-neutral use of a single fixed strategy|generally backwards chaining|to build their explanations. This paper argues that explanation should be modeled as a goal-driven learning process for gathering and transforming information, and discusses the issues involved in developing an active multi-strategy process for goal-driven explanation.

Connected through hyperedge HE-1 (induced by source paper "Goal-Driven Learning: Fundamental Issues (A Symposium Report)"):
  with members:
    - "Towards A Computer Model of Memory Search Strategy Learning"
    - "BECOMING AN EXPERT CASE-BASED REASONER: LEARNING TO ADAPT PRIOR CASES"

Connected through hyperedge HE-2 (induced by source paper "AN ARCHITECTURE FOR GOAL-DRIVEN EXPLANATION"):
  with members:
    (none)

Connected through hyperedge HE-3 (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"
    - "Learning Problem-Solving Concepts by Reflecting on Problem Solving"

Connected through hyperedge HE-4 (induced by source paper "INFERENTIAL THEORY OF LEARNING: Developing Foundations for Multistrategy Learning"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "A theory of questions and question asking"
    - "In Machine Learning: A Multistrategy Approach, Vol. IV  Macro and Micro Perspectives of Multistrategy Learning"
    - "The Use of Explicit Goals for Knowledge to Guide Inference and Learning"
    - "Context-Based Similarity Applied to Retrieval of Relevant Cases"
    - "Hybrid Learning Using Genetic Algorithms and Decision Trees for Pattern Classification"
    - "Machine Learning and Inference"
    - "Machine Learning and Inference"

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