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 Experimental Analysis of Schema Creation, Propagation and Disruption in Genetic Programming"
  Abstract: In this paper we first review the main results in the theory of schemata in Genetic Programming (GP) and summarise a new GP schema theory which is based on a new definition of schema. Then we study the creation, propagation and disruption of this new form of schemata in real runs, for standard crossover, one-point crossover and selection only. Finally, we discuss these results in the light our GP schema theorem.

Connected through hyperedge HE-1 (induced by source paper "Genetic Programming with One-Point Crossover and Point Mutation"):
  with members:
    - "An Analysis of the MAX Problem in Genetic Programming hold only in some cases, in"
    - "Price's Theorem and the MAX Problem"
    - "Why Ants are Hard genetic programming, simulated annealing and hill climbing performance is shown not"

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 "Evolution-based Discovery of Hierarchical Behaviors"):
  with members:
    - "Genetic Programming Exploratory Power and the Discovery of Functions"
    - "Evaluation and Selection of Biases in Machine Learning"

Connected through hyperedge HE-4 (induced by source paper "How Fitness Structure Affects Subsolution Acquisition in Genetic Programming"):
  with members:
    - "The Impact of External Dependency in Genetic Programming Primitives"
    - "Using a Distance Metric on Genetic Programs to Understand Genetic Operators"
    - "Neural Programming and an Internal Reinforcement Policy"
    - "Where Does the Good Stuff Go, and Why? How contextual semantics influences program structure in"

Connected through hyperedge HE-5 (induced by source paper "The Schema Theorem and Price's Theorem"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "The Troubling Aspects of a Building Block Hypothesis for Genetic Programming"
    - "Modeling Building-Block Interdependency  Dynamical and Evolutionary Machine Organization Group"
    - "Fitness Landscapes and Difficulty in Genetic Programming"
    - "Evolution in Time and Space The Parallel Genetic Algorithm"
    - "An Analysis of the MAX Problem in Genetic Programming hold only in some cases, in"
    - "Price's Theorem and the MAX Problem"

Connected through hyperedge HE-6 (induced by source paper "The Troubling Aspects of a Building Block Hypothesis for Genetic Programming"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "Genetic Programming Exploratory Power and the Discovery of Functions"
    - "Program Search with a Hierarchical Variable Length Representation: Genetic Programming, Simulated Annealing and Hill Climbing"
    - "The Royal Road for Genetic Algorithms: Fitness Landscapes and GA Performance"
    - "Towards Automatic Discovery of Building Blocks in Genetic Programming"
    - "The Schema Theorem and Price's Theorem"
    - "A Comparison of Crossover and Mutation in Genetic Programming"
    - "An Analysis of the MAX Problem in Genetic Programming hold only in some cases, in"

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