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: "Adaptation of Genetic Algorithms for Engineering Design Optimization"
  Abstract: Genetic algorithms have been extensively used in different domains as a means of doing global optimization in a simple yet reliable manner. However, in some realistic engineering design optimization domains it was observed that a simple classical implementation of the GA based on binary encoding and bit mutation and crossover was sometimes inefficient and unable to reach the global optimum. Using floating point representation alone does not eliminate the problem. In this paper we describe a way of augmenting the GA with new operators and strategies that take advantage of the structure and properties of such engineering design domains. Empirical results (initially in the domain of conceptual design of supersonic transport aircraft and the domain of high performance supersonic missile inlet design) demonstrate that the newly formulated GA can be significantly better than the classical GA in terms of efficiency and reliability. http://www.cs.rutgers.edu/~shehata/papers.html

Connected through hyperedge HE-1 (induced by source paper "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."):
  with members:
    - "Measuring the Difficulty of Specific Learning Problems"
    - "Trading Spaces: Computation, Representation and the Limits of Uninformed Learning"
    - "Discovering Complex Othello Strategies Through Evolutionary Neural Networks"
    - "USING MARKER-BASED GENETIC ENCODING OF NEURAL NETWORKS TO EVOLVE FINITE-STATE BEHAVIOUR"
    - "Evolving Obstacle Avoidance Behavior in a Robot Arm"
    - "A Comparison of Selection Schemes used in Genetic Algorithms"
    - "Determining Successful Negotiation Strategies: An Evolutionary Approach"
    - "Genetic Programming and Redundancy"

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"
    - "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 Model Selection for Hillclimbing Search in Computer-Aided Design"
    - "Intelligent Gradient-Based Search of Incompletely Defined Design Spaces"

Connected through hyperedge HE-3 (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."
    - "A Genetic Algorithm for Continuous Design Space Search"
    - "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: Genetic_Algorithms