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: "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.

Connected through hyperedge HE-1 (induced by source paper "Evolving Compact Solutions in Genetic Programming: A Case Study"):
  with members:
    - "A Comparison of Selection Schemes used in Genetic Algorithms"
    - "Genetic Programming and Redundancy"
    - "Causality in Genetic Programming"
    - "Balancing Accuracy and Parsimony in Genetic Programming 1"

Connected through hyperedge HE-2 (induced by source paper "Culling Teaching -1 Culling and Teaching in Neuro-evolution"):
  with members:
    - "References elements that can solve difficult learning control problems. on Simulation of Adaptive Behavior, pages"
    - "Genes, Phenes and the Baldwin Effect: Learning and Evolution in a Simulated Population"
    - "Cellular Encoding Applied to Neurocontrol"

Connected through hyperedge HE-3 (induced by source paper "Fitness Landscapes and Difficulty in Genetic Programming"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "Genetic Programming and Redundancy"
    - "Effects of Occam's Razor in Evolving Sigma-Pi Neural Nets"
    - "The Schema Theorem and Price's Theorem"
    - "A Simulation of Adaptive Agents in a Hostile Environment"
    - "Toward Simulated Evolution of Machine-Language Iteration"
    - "Evolving Non-Determinism: An Inventive and Efficient Tool for Optimization and Discovery of Strategies"
    - "Incremental Co-evolution of Organisms: A New Approach for Optimization and Discovery of Strategies"

Connected through hyperedge HE-4 (induced by source paper "A Study in Program Response and the Negative Effects of Introns in Genetic Programming"):
  with members:
    - "Causality in Genetic Programming"
    - "A Comparison of Crossover and Mutation in Genetic Programming"

Connected through hyperedge HE-5 (induced by source paper "Signal Path Oriented Approach for Generation of Dynamic Process Models"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."

Connected through hyperedge HE-6 (induced by source paper "A Survey of Intron Research in Genetics"):
  with members:
    - "Empirical studies of the genetic algorithm with non-coding segments"
    - "Duplication of Coding Segments in Genetic Programming"
    - "A comparison of the fixed and floating building block representation in the genetic algorithm"
    - "Evolving Turing-Complete Programs for a Register Machine with Self-modifying Code"

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