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: "An Adaptive Penalty Approach for Constrained Genetic-Algorithm Optimization"
  Abstract: In this paper we describe a new adaptive penalty approach for handling constraints in genetic algorithm optimization problems. The idea is to start with a relatively small penalty coefficient and then increase it or decrease it on demand as the optimization progresses. Empirical results in several engineering design domains demonstrate the merit of the proposed approach.

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:
    - "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"
    - "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 "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."
    - "Guided Crossover: A New Operator for Genetic Algorithm Based 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