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: "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.

Connected through hyperedge HE-1 (induced by source paper "A Transformation System for Interactive Reformulation of Design Optimization Strategies"):
  with members:
    - "Intelligent Model Selection for Hillclimbing Search in Computer-Aided Design"

Connected through hyperedge HE-2 (induced by source paper "Induction of Oblique Decision Trees"):
  with members:
    - "A System for Induction of Oblique Decision Trees"
    - "An Improved Algorithm for Incremental Induction of Decision Trees"
    - "PATTERN RECOGNITION VIA LINEAR PROGRAMMING THEORY AND APPLICATION TO MEDICAL DIAGNOSIS"
    - "Misclassification Minimization"
    - "Misclassification Minimization"
    - "Misclassification Minimization"
    - "Mingers, 1989 J. Mingers. An empirical comparison of pruning methods for decision tree induction. Machine"
    - "Lookahead and Pathology in Decision Tree Induction"

Connected through hyperedge HE-3 (induced by source paper "Learning Prototype-Selection Rules for Case-Based Iterative Design seen as a case-based reasoning system [4], in"):
  with members:
    - "Using Modeling Knowledge to Guide Design Space Search"
    - "Abstraction and Decomposition in Hillclimbing Design Optimization"

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