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: "Analysis of Neurocontrollers Designed by Simulated Evolution"
  Abstract: Randomized, adaptive, greedy search using evolutionary algorithms offers a powerful and versatile approach to the automated design of neural network architectures for a variety of tasks in artificial intelligence and robotics. In this paper we present results from the evolutionary design of a neuro-controller for a robotic bulldozer. This robot is given the task of clearing an arena littered with boxes by pushing boxes to the sides. Through a careful analysis of the evolved networks we show how evolution exploits the design constraints and properties of the environment to produce network structures of high fitness. We conclude with a brief summary of related ongoing research examining the intricate interplay between environment and evolutionary processes in determining the structure and function of the resulting neural architectures.

Connected through hyperedge HE-1 (induced by source paper "Properties of Genetic Representations of Neural Architectures"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "Generative Learning Structures and Processes for Generalized Connectionist Networks"
    - "Analysis of Decision Boundaries Generated by Constructive Neural Network Learning Algorithms"

Connected through hyperedge HE-2 (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-3 (induced by source paper "Issues in Evolutionary Robotics"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "Evolving Obstacle Avoidance Behavior in a Robot Arm"
    - "The Evolutionary Cost of Learning"
    - "Selection for Wandering Behavior in a Small Robot"
    - "A Hybrid GP/GA Approach for Co-evolving Controllers and Robot Bodies to Achieve Fitness-Specified Tasks"
    - "Environmental Effects on Minimal Behaviors in the Minimat World"
    - "Evolving nonTrivial Behaviors on Real Robots: an Autonomous Robot that Picks up Objects"
    - "Evolving Visually Guided Robots"

Connected through hyperedge HE-4 (induced by source paper "The Automatic Programming of Agents that Learn Mental Models and Create Simple Plans of Action"):
  with members:
    - "Evolution of Iteration in Genetic Programming D a v d A The solution to many"
    - "20 Data Structures and Genetic Programming  two techniques for reducing run time."
    - "Evolution of Mapmaking: Learning, planning, and memory using Genetic Programming"
    - "A Comparison of Crossover and Mutation in Genetic Programming"
    - "Cultural Transmission of Information in Genetic Programming"
    - "Evolving Teamwork and Coordination with Genetic Programming"
    - "Culture Enhances the Evolvability of Cognition"
    - "Neural Programming and an Internal Reinforcement Policy"

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