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: "Small Populations over Many Generations can beat Large Populations over Few Generations in Genetic Programming"
  Abstract: This paper looks at the use of small populations in Genetic Programming (GP), where the trend in the literature appears to be towards using as large a population as possible, which requires more memory resources and CPU-usage is less efficient. Dynamic Subset Selection (DSS) and Limited Error Fitness (LEF) are two different, adaptive variations of the standard supervised learning method used in GP. This paper compares the performance of GP, GP+DSS, and GP+LEF, on a 958 case classification problem, using a small population size of 50. A similar comparison between GP and GP+DSS is done on a larger and messier 3772 case classification problem. For both problems, GP+DSS with the small population size consistently produces a better answer using fewer tree evaluations than other runs using much larger populations. Even standard GP can be seen to perform well with the much smaller population size, indicating that it is certainly worth an exploratory run or three with a small population size before assuming that a large population size is necessary. It is an interesting notion that smaller can mean faster and better.

Connected through hyperedge HE-1 (induced by source paper "Some Training Subset Selection Methods for Supervised Learning in Genetic Programming"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "Tackling the Boolean Even N Parity Problem with Genetic Programming and Limited-Error Fitness standard GP"

Connected through hyperedge HE-2 (induced by source paper "Tackling the Boolean Even N Parity Problem with Genetic Programming and Limited-Error Fitness standard GP"):
  with members:
    - "New Methods for Competitive Coevolution"
    - "A Comparison of Selection Schemes used in Genetic Algorithms"
    - "Some Training Subset Selection Methods for Supervised Learning in Genetic Programming"

Connected through hyperedge HE-3 (induced by source paper "New Methods for Competitive Coevolution"):
  with members:
    - "A Competitive Approach to Game Learning"
    - "Tackling the Boolean Even N Parity Problem with Genetic Programming and Limited-Error Fitness standard GP"
    - "Every Niching Method has its Niche: Fitness Sharing and Implicit Sharing Compared"
    - "17 Massively Parallel Genetic Programming"
    - "Automatic Modularization by Speciation"
    - "Dynamics of Co-evolutionary Learning"

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