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: "Causality in Genetic Programming"
  Abstract: Causality relates changes in the structure of an object with the effects of such changes, that is changes in the properties or behavior of the object. This paper analyzes the concept of causality in Genetic Programming (GP) and suggests how it can be used in adapting control parameters for speeding up GP search. We first analyze the effects of crossover to show the weak causality of the GP representation and operators. Hierarchical GP approaches based on the discovery and evolution of functions amplify this phenomenon. However, selection gradually retains strongly causal changes. Causality is correlated to search space exploitation and is discussed in the context of the exploration-exploitation tradeoff. The results described argue for a bottom-up GP evolutionary thesis. Finally, new developments based on the idea of GP architecture evolution (Koza, 1994a) are discussed from the causality perspective.

Connected through hyperedge HE-1 (induced by source paper "Genetic Programming Exploratory Power and the Discovery of Functions"):
  with members:
    - "The Troubling Aspects of a Building Block Hypothesis for Genetic Programming"
    - "Evolution-based Discovery of Hierarchical Behaviors"

Connected through hyperedge HE-2 (induced by source paper "Hierarchical Self-Organization in Genetic Programming"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."

Connected through hyperedge HE-3 (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"
    - "Complexity Compression and Evolution"
    - "Balancing Accuracy and Parsimony in Genetic Programming 1"

Connected through hyperedge HE-4 (induced by source paper "Towards Automatic Discovery of Building Blocks in Genetic Programming"):
  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"
    - "Strongly Typed Genetic Programming"

Connected through hyperedge HE-5 (induced by source paper "Genetic Programming and Redundancy"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "A Comparison of Selection Schemes used in Genetic Algorithms"
    - "Evolving Compact Solutions in Genetic Programming: A Case Study"
    - "Fitness Landscapes and Difficulty in Genetic Programming"
    - "Position Paper, Workshop on Evolutionary Computation with Variable Size Representation, ICGA, Fitness Causes Bloat in"
    - "An Adverse Interaction between the Crossover Operator and a Restriction on Tree Depth of Crossover"
    - "The MAX Problem for Genetic Programming Highlighting an Adverse Interaction between the Crossover Operator and"

Connected through hyperedge HE-6 (induced by source paper "A Study in Program Response and the Negative Effects of Introns in Genetic Programming"):
  with members:
    - "Complexity Compression and Evolution"
    - "A Comparison of Crossover and Mutation in Genetic Programming"

Connected through hyperedge HE-7 (induced by source paper "Position Paper, Workshop on Evolutionary Computation with Variable Size Representation, ICGA, Fitness Causes Bloat in"):
  with members:
    - "Genetic Programming and Redundancy"
    - "Genetic Programming Bloat with Dynamic Fitness"

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