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: "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.

Connected through hyperedge HE-1 (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"
    - "Evolving Non-Determinism: An Inventive and Efficient Tool 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-2 (induced by source paper "Evolving Non-Determinism: An Inventive and Efficient Tool for Optimization and Discovery of Strategies"):
  with members:
    - "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-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"
    - "Evolving Non-Determinism: An Inventive and Efficient Tool for Optimization and Discovery of Strategies"
    - "Complexity Compression and Evolution"

Connected through hyperedge HE-4 (induced by source paper "Dynamic Parameter Encoding for Genetic Algorithms"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "On The State of Evolutionary Computation"
    - "Representation and Evolution of Neural Networks"
    - "GENETIC ALGORITHMS AND VERY FAST SIMULATED REANNEALING: A COMPARISON"
    - "Dynamic Control of Genetic Algorithms using Fuzzy Logic Techniques"
    - "Evolution of Non-Deterministic Incremental Algorithms as a New Approach for Search in State Spaces"

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