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: "Intelligent Model Selection for Hillclimbing Search in Computer-Aided Design"
  Abstract: Models of physical systems can differ according to computational cost, accuracy and precision, among other things. Depending on the problem solving task at hand, different models will be appropriate. Several investigators have recently developed methods of automatically selecting among multiple models of physical systems. Our research is novel in that we are developing model selection techniques specifically suited to computer-aided de sign. Our approach is based on the idea that artifact performance models for computer-aided design should be chosen in light of the design decisions they are required to support. We have developed a technique called "Gradient Magnitude Model Selection" (GMMS), which embodies this principle. GMMS operates in the context of a hillclimbing search process. It selects the simplest model that meets the needs of the hillclimbing algorithm in which it operates. We are using the domain of sailing yacht design as a testbed for this research. We have implemented GMMS and used it in hillclimb-ing search to decide between a computationally expensive potential-flow program and an algebraic approximation to analyze the performance of sailing yachts. Experimental tests show that GMMS makes the design process faster than it would be if the most expensive model were used for all design evaluations. GMMS achieves this performance improvement with little or no sacrifice in the quality of the resulting design.

Connected through hyperedge HE-1 (induced by source paper "A Transformation System for Interactive Reformulation of Design Optimization Strategies"):
  with members:
    - "Learning When Reformulation is Appropriate for Iterative Design"

Connected through hyperedge HE-2 (induced by source paper "Using Modeling Knowledge to Guide Design Space Search"):
  with members:
    - "An Adaptive Penalty Approach for Constrained Genetic-Algorithm Optimization"
    - "Adaptation of Genetic Algorithms for Engineering Design Optimization"
    - "A Genetic Algorithm for Continuous Design Space Search"
    - "Guided Crossover: A New Operator for Genetic Algorithm Based Optimization"
    - "Learning Prototype-Selection Rules for Case-Based Iterative Design seen as a case-based reasoning system [4], in"
    - "Intelligent Gradient-Based Search of Incompletely Defined Design Spaces"

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