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: "Guided Crossover: A New Operator for Genetic Algorithm Based Optimization"
  Abstract: Genetic algorithms (GAs) have been extensively used in different domains as a means of doing global optimization in a simple yet reliable manner. They have a much better chance of getting to global optima than gradient based methods which usually converge to local sub optima. However, GAs have a tendency of getting only moderately close to the optima in a small number of iterations. To get very close to the optima, the GA needs a very large number of iterations. Whereas gradient based optimizers usually get very close to local optima in a relatively small number of iterations. In this paper we describe a new crossover operator which is designed to endow the GA with gradient-like abilities without actually computing any gradients and without sacrificing global optimality. The operator works by using guidance from all members of the GA population to select a direction for exploration. Empirical results in two engineering design domains and across both binary and floating point representations demonstrate that the operator can significantly improve the steady state error of the GA optimizer.

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"
    - "A Genetic Algorithm for Continuous Design Space Search"
    - "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 "Adaptation of Genetic Algorithms for Engineering Design 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"

Connected through hyperedge HE-4 (induced by source paper "A Genetic Algorithm for Continuous Design Space Search"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "An Adaptive Penalty Approach for Constrained Genetic-Algorithm Optimization"
    - "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