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 "A Transformation System for Interactive Reformulation of Design Optimization Strategies"):
  Abstract: Numerical design optimization algorithms are highly sensitive to the particular formulation of the optimization problems they are given. The formulation of the search space, the objective function and the constraints will generally have a large impact on the duration of the optimization process as well as the quality of the resulting design. Furthermore, the best formulation will vary from one application domain to another, and from one problem to another within a given application domain. Unfortunately, a design engineer may not know the best formulation in advance of attempting to set up and run a design optimization process. In order to attack this problem, we have developed a software environment that supports interactive formulation, testing and reformulation of design optimization strategies. Our system represents optimization strategies in terms of second-order dataflow graphs. Reformulations of strategies are implemented as transformations between dataflow graphs. The system permits the user to interactively generate and search a space of design optimization strategies, and experimentally evaluate their performance on test problems, in order to find a strategy that is suitable for his application domain. The system has been implemented in a domain independent fashion, and is being tested in the domain of racing yacht design.

Includes members:
  Member "Learning When Reformulation is Appropriate for Iterative Design":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Induction of Oblique Decision Trees")
  Member "Intelligent Model Selection for Hillclimbing Search in Computer-Aided Design":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Using Modeling Knowledge to Guide Design Space Search")

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: Genetic_Algorithms