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: "Evolving Non-Determinism: An Inventive and Efficient Tool 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 proposes an inventive way to explore the space of states that, combined with the use of simulated co-evolution, 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.

Connected through hyperedge HE-1 (induced by source paper "Incremental Co-evolution of Organisms: A New Approach for Optimization and Discovery of Strategies"):
  with members:
    - "Dynamic Parameter Encoding for Genetic Algorithms"
    - "Fitness Landscapes and Difficulty in Genetic Programming"
    - "Evolution of Non-Deterministic Incremental Algorithms as a New Approach for Search in State Spaces"

Connected through hyperedge HE-2 (induced by source paper "Evolution of Non-Deterministic Incremental Algorithms as a New Approach for Search in State Spaces"):
  with members:
    - "A Stochastic Search Approach to Grammar Induction"
    - "Dynamic Parameter Encoding for Genetic Algorithms"
    - "A Survey of Evolution Strategies"
    - "Incremental Co-evolution of Organisms: A New Approach for Optimization and Discovery of Strategies"
    - "Evolving Sorting Networks using Genetic Programming and Rapidly Reconfigurable Field-Programmable Gate Arrays Convergent Design, L.L.C."
    - "Use of Architecture-Altering Operations to Dynamically Adapt a Three-Way Analog Source Identification Circuit to Accommodate"

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"
    - "Incremental Co-evolution of Organisms: A New Approach for Optimization and Discovery of Strategies"
    - "Complexity Compression and Evolution"

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