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: "Supporting Combined Human and Machine Planning: An Interface for Planning by Analogical Reasoning"
  Abstract: Realistic and complex planning situations require a mixed-initiative planning framework in which human and automated planners interact to mutually construct a desired plan. Ideally, this joint cooperation has the potential of achieving better plans than either the human or the machine can create alone. Human planners often take a case-based approach to planning, relying on their past experience and planning by retrieving and adapting past planning cases. Planning by analogical reasoning in which generative and case-based planning are combined, as in Prodigy/Analogy, provides a suitable framework to study this mixed-initiative integration. However, having a human user engaged in this planning loop creates a variety of new research questions. The challenges we found creating a mixed-initiative planning system fall into three categories: planning paradigms differ in human and machine planning; visualization of the plan and planning process is a complex, but necessary task; and human users range across a spectrum of experience, both with respect to the planning domain and the underlying planning technology. This paper presents our approach to these three problems when designing an interface to incorporate a human into the process of planning by analogical reasoning with Prodigy/Analogy. The interface allows the user to follow both generative and case-based planning, it supports visualization of both plan and the planning rationale, and it addresses the variance in the experience of the user by allowing the user to control the presentation of information.

Connected through hyperedge HE-1 (induced by source paper "A Case Study of Case-Based CBR"):
  with members:
    - "Acquiring Case Adaptation Knowledge: A Hybrid Approach"
    - "Learning to Integrate Multiple Knowledge Sources for Case-Based Reasoning"
    - "Case-Based Similarity Assessment: Estimating Adaptability from Experience"
    - "Case-Based Learning: Beyond Classification of Feature Vectors"

Connected through hyperedge HE-2 (induced by source paper "S o l u t i o n Relevant A b s t r a"):
  with members:
    - "Structural Similarity as Guidance in Case-Based Design"

Connected through hyperedge HE-3 (induced by source paper "Supporting Combined Human and Machine Planning: The Prodigy 4.0 User Interface Version 2.0*"):
  with members:
    - "Merge Strategies for Multiple Case Plan Replay"
    - "Towards Mixed-Initiative Rationale-Supported Planning"

Connected through hyperedge HE-4 (induced by source paper "Merge Strategies for Multiple Case Plan Replay"):
  with members:
    - "Evaluating the Effectiveness of Derivation Replay in Partial-order vs State-space Planning"
    - "Supporting Combined Human and Machine Planning: The Prodigy 4.0 User Interface Version 2.0*"

Connected through hyperedge HE-5 (induced by source paper "Towards Mixed-Initiative Rationale-Supported Planning"):
  with members:
    - "Supporting Combined Human and Machine Planning: The Prodigy 4.0 User Interface Version 2.0*"

Connected through hyperedge HE-6 (induced by source paper "Learning to Integrate Multiple Knowledge Sources for Case-Based Reasoning"):
  with members:
    - "Constructive Similarity Assessment: Using Stored Cases to Define New Situations"
    - "In Machine Learning: A Multistrategy Approach, Vol. IV  Macro and Micro Perspectives of Multistrategy Learning"
    - "Acquiring Case Adaptation Knowledge: A Hybrid Approach"
    - "Case-Based Similarity Assessment: Estimating Adaptability from Experience"
    - "A Case Study of Case-Based CBR"

Connected through hyperedge HE-7 (induced by source paper "Learning to Improve Case Adaptation by Introspective Reasoning and CBR"):
  with members:
    - "Representing Self-knowledge for Introspection about Memory Search"
    - "Towards Improving Case Adaptability with a Genetic Algorithm"
    - "Acquiring Case Adaptation Knowledge: A Hybrid Approach"
    - "Combining Rules and Cases to Learn Case Adaptation"
    - "Towards A Computer Model of Memory Search Strategy Learning"

Connected through hyperedge HE-8 (induced by source paper "On the Usefulness of Re-using Diagnostic Solutions"):
  with members:
    - "A utility-based approach to learning in a mixed Case-Based and Model-Based Reasoning architecture"
    - "ADAPtER: an Integrated Diagnostic System Combining Case-Based and Abductive Reasoning"

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