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 "Learning When Reformulation is Appropriate for Iterative Design"):
  Abstract: It is well known that search-space reformulation can improve the speed and reliability of numerical optimization in engineering design. We argue that the best choice of reformulation depends on the design goal, and present a technique for automatically constructing rules that map the design goal into a reformulation chosen from a space of possible reformulations. We tested our technique in the domain of racing-yacht-hull design, where each reformulation corresponds to incorporating constraints into the search space. We applied a standard inductive-learning algorithm, C4.5, to a set of training data describing which constraints are active in the optimal design for each goal encountered in a previous design session. We then used these rules to choose an appropriate reformulation for each of a set of test cases. Our experimental results show that using these reformulations improves both the speed and the reliability of design optimization, outperforming competing methods and approaching the best performance possible.

Includes members:
  Member "Induction of Oblique Decision Trees":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Mingers, 1989 J. Mingers. An empirical comparison of pruning methods for decision tree induction. Machine")
  Member "Learning Prototype-Selection Rules for Case-Based Iterative Design seen as a case-based reasoning system [4], in":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Using Modeling Knowledge to Guide Design Space Search")
  Member "A Transformation System for Interactive Reformulation of Design Optimization Strategies":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Intelligent Model Selection for Hillclimbing Search in Computer-Aided Design")

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