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: "A Genetic Algorithm for Continuous Design Space Search"
  Abstract: Genetic algorithms (GAs) have been extensively used as a means for performing global optimization in a simple yet reliable manner. However, in some realistic engineering design optimization domains the simple, classical implementation of a GA based on binary encoding and bit mutation and crossover is often inefficient and unable to reach the global optimum. In this paper we describe a GA for continuous design-space optimization that uses new GA operators and strategies tailored to the structure and properties of engineering design domains. Empirical results in the domains of supersonic transport aircraft and supersonic missile inlets demonstrate that the newly formulated GA can be significantly better than the classical GA in both efficiency and reliability.

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"
    - "Adaptation of Genetic Algorithms for Engineering Design Optimization"
    - "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."
    - "Adaptation of Genetic Algorithms for Engineering Design Optimization"
    - "Using Modeling Knowledge to Guide Design Space Search"

Connected through hyperedge HE-4 (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."
    - "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