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 "Incremental Co-evolution of Organisms: A New Approach for Optimization and Discovery of Strategies"):
  Abstract: In the field of optimization and machine learning techniques, some very efficient and promising tools like Genetic Algorithms (GAs) and Hill-Climbing have been designed. In this same field, the Evolving Non-Determinism (END) model presented in this paper proposes an inventive way to explore the space of states that, using the simulated "incremental" co-evolution of some organisms, remedies some drawbacks of these previous techniques and even allow this model to outperform them on some difficult problems. This new model has been applied to the sorting network problem, a reference problem that challenged many computer scientists, and an original one-player game named Solitaire. For the first problem, the END model has been able to build from "scratch" some sorting networks as good as the best known for the 16-input problem. It even improved by one comparator a 25 years old result for the 13-input problem. For the Solitaire game, END evolved a strategy comparable to a human designed strategy.

Includes members:
  Member "Dynamic Parameter Encoding for Genetic Algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-1 (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-2 (induced by source paper "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley.")
  Member "Evolving Non-Determinism: An Inventive and Efficient Tool for Optimization and Discovery of Strategies":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Evolution of Non-Deterministic Incremental Algorithms as a New Approach for Search in State Spaces")
  Member "Evolution of Non-Deterministic Incremental Algorithms as a New Approach for Search in State Spaces":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "Evolving Sorting Networks using Genetic Programming and Rapidly Reconfigurable Field-Programmable Gate Arrays Convergent Design, L.L.C.")

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