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: "Abstraction and Decomposition in Hillclimbing Design Optimization"
  Abstract: The performance of hillclimbing design optimization can be improved by abstraction and decomposition of the design space. Methods for automatically finding and exploiting such abstractions and decompositions are presented in this paper. A technique called "Operator Importance Analysis" finds useful abstractions. It does so by determining which of a given set of operators are the most important for a given class of design problems. Hillclimbing search runs faster when performed using this this smaller set of operators. A technique called "Operator Interaction Analysis" finds useful decompositions. It does so by measuring the pairwise interaction between operators. It uses such measurements to form an ordered partition of the operator set. This partition can then be used in a "hierarchic" hillclimbing algorithm which runs faster than ordinary hillclimbing with an unstructured operator set. We have implemented both techniques and tested them in the domain of racing yacht hull design. Our experimental results show that these two methods can produce substantial speedups with little or no loss in quality of the resulting designs.

Connected through hyperedge HE-1 (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"
    - "Using Modeling Knowledge to Guide Design Space Search"

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