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: "Price's Theorem and the MAX Problem"
  Abstract: We present a detailed analysis of the evolution of GP populations using the problem of finding a program which returns the maximum possible value for a given terminal and function set and a depth limit on the program tree (known as the MAX problem). We confirm the basic message of [ Gathercole and Ross, 1996 ] that crossover together with program size restrictions can be responsible for premature convergence to a sub-optimal solution. We show that this can happen even when the population retains a high level of variety and show that in many cases evolution from the sub-optimal solution to the solution is possible if sufficient time is allowed. In both cases theoretical models are presented and compared with actual runs. Experimental evidence is presented that Price's Covariance and Selection Theorem can be applied to GP populations and the practical effect of program size restrictions are noted. Finally we show that covariance between gene frequency and fitness in the first few generations can be used to predict the course of GP runs.

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

Connected through hyperedge HE-2 (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"
    - "An Experimental Analysis of Schema Creation, Propagation and Disruption 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"

Connected through hyperedge HE-3 (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"
    - "An Experimental Analysis of Schema Creation, Propagation and Disruption in Genetic Programming"
    - "A Comparison of Crossover and Mutation in Genetic Programming"

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