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: "Discovering Complex Othello Strategies Through Evolutionary Neural Networks"
  Abstract: An approach to develop new game playing strategies based on artificial evolution of neural networks is presented. Evolution was directed to discover strategies in Othello against a random-moving opponent and later against an ff-fi search program. The networks discovered first a standard positional strategy, and subsequently a mobility strategy, an advanced strategy rarely seen outside of tournaments. The latter discovery demonstrates how evolutionary neural networks can develop novel solutions by turning an initial disadvantage into an advantage in a changed environment.

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"
    - "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"
    - "Genetic Programming and Redundancy"
    - "Island Model Genetic Algorithms and Linearly Separable Problems"

Connected through hyperedge HE-2 (induced by source paper "Real-time Interactive Neuro-evolution"):
  with members:
    - "Machine Learning,  Efficient Reinforcement Learning through Symbiotic Evolution"
    - "Incremental Evolution of Complex General Behavior"
    - "Evolving Neural Networks to Play Go"

Connected through hyperedge HE-3 (induced by source paper "USING MARKER-BASED GENETIC ENCODING OF NEURAL NETWORKS TO EVOLVE FINITE-STATE BEHAVIOUR"):
  with members:
    - "References elements that can solve difficult learning control problems. on Simulation of Adaptive Behavior, pages"
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "Grounding Robotic Control with Genetic Neural Networks"
    - "Evolving Graphs and Networks with Edge Encoding: Preliminary Report"

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