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: "Using Modeling Knowledge to Guide Design Space Search"
  Abstract: Automated search of a space of candidate designs seems an attractive way to improve the traditional engineering design process. To make this approach work, however, the automated design system must include both knowledge of the modeling limitations of the method used to evaluate candidate designs and also an effective way to use this knowledge to influence the search process. We suggest that a productive approach is to include this knowledge by implementing a set of model constraint functions which measure how much each modeling assumptions is violated, and to influence the search by using the values of these model constraint functions as constraint inputs to a standard constrained nonlinear optimization numerical method. We test this idea in the domain of conceptual design of supersonic transport aircraft, and our experiments indicate that our model constraint communication strategy can decrease the cost of design space search by one or more orders of magnitude.

Connected through hyperedge HE-1 (induced by source paper "Guided Crossover: A New Operator for Genetic Algorithm Based Optimization"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "Adaptation of Genetic Algorithms for Engineering Design Optimization"
    - "A Genetic Algorithm for Continuous Design Space Search"

Connected through hyperedge HE-2 (induced by source paper "An Adaptive Penalty Approach for Constrained Genetic-Algorithm Optimization"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "A Genetic Algorithm for Continuous Design Space Search"

Connected through hyperedge HE-3 (induced by source paper "Adaptation of Genetic Algorithms for Engineering Design Optimization"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "Guided Crossover: A New Operator for Genetic Algorithm Based Optimization"

Connected through hyperedge HE-4 (induced by source paper "A Genetic Algorithm for Continuous Design Space Search"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "An Adaptive Penalty Approach for Constrained Genetic-Algorithm Optimization"
    - "Guided Crossover: A New Operator for Genetic Algorithm Based Optimization"

Connected through hyperedge HE-5 (induced by source paper "Intelligent Model Selection for Hillclimbing Search in Computer-Aided Design"):
  with members:
    - "A Transformation System for Interactive Reformulation of Design Optimization Strategies"

Connected through hyperedge HE-6 (induced by source paper "Intelligent Gradient-Based Search of Incompletely Defined Design Spaces"):
  with members:
    (none)

Connected through hyperedge HE-7 (induced by source paper "Learning Prototype-Selection Rules for Case-Based Iterative Design seen as a case-based reasoning system [4], in"):
  with members:
    - "Learning When Reformulation is Appropriate for Iterative Design"
    - "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: Genetic_Algorithms