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: "Genetic Programming and Redundancy"
  Abstract: The Genetic Programming optimization method (GP) elaborated by John Koza [ Koza, 1992 ] is a variant of Genetic Algorithms. The search space of the problem domain consists of computer programs represented as parse trees, and the crossover operator is realized by an exchange of subtrees. Empirical analyses show that large parts of those trees are never used or evaluated which means that these parts of the trees are irrelevant for the solution or redundant. This paper is concerned with the identification of the redundancy occuring in GP. It starts with a mathematical description of the behavior of GP and the conclusions drawn from that description among others explain the "size problem" which denotes the phenomenon that the average size of trees in the population grows with time.

Connected through hyperedge HE-1 (induced by source paper "A Comparison of Selection Schemes used in Genetic Algorithms"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "Determining Successful Negotiation Strategies: An Evolutionary Approach"
    - "Overview of Selection Schemes and a Suggested Classification"
    - "Tackling the Boolean Even N Parity Problem with Genetic Programming and Limited-Error Fitness standard GP"
    - "Evolving Compact Solutions in Genetic Programming: A Case Study"

Connected through hyperedge HE-2 (induced by source paper "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."):
  with members:
    - "Measuring the Difficulty of Specific Learning Problems"
    - "Trading Spaces: Computation, Representation and the Limits of Uninformed Learning"
    - "Discovering Complex Othello Strategies Through Evolutionary Neural Networks"
    - "USING MARKER-BASED GENETIC ENCODING OF NEURAL NETWORKS TO EVOLVE FINITE-STATE BEHAVIOUR"
    - "Evolving Obstacle Avoidance Behavior in a Robot Arm"
    - "A Comparison of Selection Schemes used in Genetic Algorithms"
    - "Determining Successful Negotiation Strategies: An Evolutionary Approach"
    - "Island Model Genetic Algorithms and Linearly Separable Problems"

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"
    - "Causality in Genetic Programming"
    - "Complexity Compression and Evolution"
    - "Balancing Accuracy and Parsimony in Genetic Programming 1"

Connected through hyperedge HE-4 (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."
    - "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"
    - "Complexity Compression and Evolution"

Connected through hyperedge HE-5 (induced by source paper "Causality in Genetic Programming"):
  with members:
    - "Evolving Compact Solutions in Genetic Programming: A Case Study"
    - "Genetic Programming Exploratory Power and the Discovery of Functions"
    - "Hierarchical Self-Organization in Genetic Programming"
    - "Towards Automatic Discovery of Building Blocks in Genetic Programming"
    - "A Study in Program Response and the Negative Effects of Introns in Genetic Programming"
    - "Position Paper, Workshop on Evolutionary Computation with Variable Size Representation, ICGA, Fitness Causes Bloat in"

Connected through hyperedge HE-6 (induced by source paper "An Adverse Interaction between the Crossover Operator and a Restriction on Tree Depth of Crossover"):
  with members:
    - "Context Preserving Crossover in Genetic Programming."
    - "Hierarchical Genetic Programming (HGP) extensions discover, modify, and exploit subroutines to accelerate the evolution of"
    - "Hybridized Crossover-Based Search Techniques for Program Discovery"

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:
    - "Causality in Genetic Programming"
    - "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