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: "Evolutionary Computation in Air Traffic Control Planning"
  Abstract: Air Traffic Control is involved in the real-time planning of aircraft trajectories. This is a heavily constrained optimization problem. We concentrate on free-route planning, in which aircraft are not required to fly over way points. The choice of a proper representation for this real-world problem is non-trivial. We propose a two level representation: one level on which the evolutionary operators work, and a derived level on which we do calculations. Furthermore we show that a specific choice of the fitness function is important for finding good solutions to large problem instances. We use a hybrid approach in the sense that we use knowledge about air traffic control by using a number of heuristics. We have built a prototype of a planning tool, and this resulted in a flexible tool for generating a free-route planning of low cost, for a number of aircraft.

Connected through hyperedge HE-1 (induced by source paper "An Evolutionary Approach to Time Constrained Routing Problems"):
  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