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 Prototype-Selection Rules for Case-Based Iterative Design seen as a case-based reasoning system [4], in"
  Abstract: The first step for most case-based design systems is to select an initial prototype from a database of previous designs. The retrieved prototype is then modified to tailor it to the given goals. For any particular design goal the selection of a starting point for the design process can have a dramatic effect both on the quality of the eventual design and on the overall design time. We present a technique for automatically constructing effective prototype-selection rules. Our technique applies a standard inductive-learning algorithm, C4.5, to a set of training data describing which particular prototype would have been the best choice for each goal encountered in a previous design session. We have tested our technique in the domain of racing-yacht-hull design, comparing our inductively learned selection rules to several competing prototype-selection methods. Our results show that the inductive prototype-selection method leads to better final designs when the design process is guided by a noisy evaluation function, and that the inductively learned rules will often be more efficient than competing methods. Many automated design systems begin by retrieving an initial prototype from a library of previous designs, using the given design goal as an index to guide the retrieval process [14]. The retrieved prototype is then modified by a set of design modification operators to tailor the selected design to the given goals. In many cases the quality of competing designs can be assessed using domain-specific evaluation functions, and in such cases the design-modification process is often This research has benefited from numerous discussions with members of the Rutgers CAP project. We thank Andrew Gelsey for helping with the cross-validation code, John Keane for helping with RUVPP, and Andrew Gelsey and Tim Weinrich for comments on a previous draft of this paper. This research was supported under ARPA-funded NASA grant NAG 2-645. In the context of such case-based design systems, the choice of an initial prototype can affect both the quality of the final design and the computational cost of obtaining that design, for three reasons. First, prototype selection may impact quality when the prototypes lie in disjoint search spaces. In particular, if the system's design modification operators cannot convert any prototype into any other prototype, the choice of initial prototype will restrict the set of possible designs that can be obtained by any search process. A poor choice of initial prototype may therefore lead to a suboptimal final design. Second, prototype selection may impact quality when the design process is guided by a nonlinear evaluation function with unknown global properties. Since there is no known method that is guaranteed to find the global optimum of an arbitrary nonlinear function [7], most design systems rely on iterative local search methods whose results are sensitive to the initial starting point. Finally, the choice of prototype may have an impact on the time needed to carry out the design modification process|two different starting points may yield the same final design but take very different amounts of time to get there. In design problems where evaluating even just a single design can take tremendous amounts of time, selecting an appropriate initial prototype can be the determining factor in the success or failure of the design process. This paper describes the application of inductive learning [11] to form rules for selecting appropriate prototype designs. The paper is structured as follows. In Section 2, we describe our inductive method for learning prototype-selection rules. In Section 3 we describe the domain of racing-yacht-hull design, in which we tested our prototype-selection methods. In Sections 4 and 5, we describe the experiments

Connected through hyperedge HE-1 (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"
    - "Intelligent Model Selection for Hillclimbing Search in Computer-Aided Design"
    - "Intelligent Gradient-Based Search of Incompletely Defined Design Spaces"

Connected through hyperedge HE-2 (induced by source paper "Abstraction and Decomposition in Hillclimbing Design Optimization"):
  with members:
    (none)

Connected through hyperedge HE-3 (induced by source paper "Learning When Reformulation is Appropriate for Iterative Design"):
  with members:
    - "Induction of Oblique Decision Trees"
    - "A Transformation System for Interactive Reformulation of Design Optimization Strategies"

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