human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge groups papers that share a common citing paper.

Details of the hypergraph:
Center hyperedge (induced by source paper "Using Case-Based Reasoning as a Reinforcement Learning Framework for Optimization with Changing Criteria"):
  Abstract: Practical optimization problems such as job-shop scheduling often involve optimization criteria that change over time. Repair-based frameworks have been identified as flexible computational paradigms for difficult combinatorial optimization problems. Since the control problem of repair-based optimization is severe, Reinforcement Learning (RL) techniques can be potentially helpful. However, some of the fundamental assumptions made by traditional RL algorithms are not valid for repair-based optimization. Case-Based Reasoning (CBR) compensates for some of the limitations of traditional RL approaches. In this paper, we present a Case-Based Reasoning RL approach, implemented in the C A B I N S system, for repair-based optimization. We chose job-shop scheduling as the testbed for our approach. Our experimental results show that C A B I N S is able to effectively solve problems with changing optimization criteria which are not known to the system and only exist implicitly in a extensional manner in the case base.

Includes members:
  Member "Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "TDLeaf(): Combining Temporal Difference learning with game-tree search.")
  Member "Transfer of Learning by Composing Solutions of Elemental Sequential Tasks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Robust Reinforcement Learning in Motion Planning")
  Member "Using Case-Based Reasoning to Acquire User Scheduling Preferences that Change over Time":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Case-based Acquisition of User Preferences for Solution Improvement in Ill-Structured Domains")
  Member "Case-based Acquisition of User Preferences for Solution Improvement in Ill-Structured Domains":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "Improving accuracy by combining rule-based and case-based reasoning")

Question: Please tell me which class the citing paper (i.e., the source paper of the center hyperedge) 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