human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge groups papers that share a common citing paper.

Details of the hypergraph:
Center hyperedge (induced by source paper "Complexity Compression and Evolution"):
  Abstract: Compression of information is an important concept in the theory of learning. We argue for the hypothesis that there is an inherent compression pressure towards short, elegant and general solutions in a genetic programming system and other variable length evolutionary algorithms. This pressure becomes visible if the size or complexity of solutions are measured without non-effective code segments called introns. The built in parsimony pressure effects complex fitness functions, crossover probability, generality, maximum depth or length of solutions, explicit parsimony, granularity of fitness function, initialization depth or length, and modulariz-ation. Some of these effects are positive and some are negative. In this work we provide a basis for an analysis of these effects and suggestions to overcome the negative implications in order to obtain the balance needed for successful evolution. An empirical investigation that supports our hypothesis is also presented.

Includes members:
  Member "Evolving Compact Solutions in Genetic Programming: A Case Study":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "A Comparison of Selection Schemes used in Genetic Algorithms")
  Member "Signal Path Oriented Approach for Generation of Dynamic Process Models":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley.")
  Member "Fitness Landscapes and Difficulty in Genetic Programming":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley.")
  Member "Culling Teaching -1 Culling and Teaching in Neuro-evolution":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "Cellular Encoding Applied to Neurocontrol")
  Member "A Study in Program Response and the Negative Effects of Introns in Genetic Programming":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "A Comparison of Crossover and Mutation in Genetic Programming")
  Member "A Survey of Intron Research in Genetics":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "Empirical studies of the genetic algorithm with non-coding segments")

Question: Please tell me which class the citing paper (i.e., the source paper of the center hyperedge) 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