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: "A Comparison of Selection Schemes used in Genetic Algorithms"
  Abstract: TIK-Report Nr. 11, December 1995 Version 2 (2. Edition)

Connected through hyperedge HE-1 (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"
    - "Determining Successful Negotiation Strategies: An Evolutionary Approach"
    - "Genetic Programming and Redundancy"
    - "Island Model Genetic Algorithms and Linearly Separable Problems"

Connected through hyperedge HE-2 (induced by source paper "Evolving Compact Solutions in Genetic Programming: A Case Study"):
  with members:
    - "Genetic Programming and Redundancy"
    - "Causality in Genetic Programming"
    - "Complexity Compression and Evolution"
    - "Balancing Accuracy and Parsimony in Genetic Programming 1"

Connected through hyperedge HE-3 (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"
    - "Some Training Subset Selection Methods for Supervised Learning in Genetic Programming"
    - "Small Populations over Many Generations can beat Large Populations over Few Generations in Genetic Programming"

Connected through hyperedge HE-4 (induced by source paper "Determining Successful Negotiation Strategies: An Evolutionary Approach"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "Genetic Algorithms, Tournament Selection, and the Effects of Noise"

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."
    - "Evolving Compact Solutions in Genetic Programming: A Case Study"
    - "Causality in Genetic Programming"
    - "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 "Overview of Selection Schemes and a Suggested Classification"):
  with members:
    (none)

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