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 Evolution of Complex General Behavior"
  Abstract: Several researchers have demonstrated how complex action sequences can be learned through neuro-evolution (i.e. evolving neural networks with genetic algorithms). However, complex general behavior such as evading predators or avoiding obstacles, which is not tied to specific environments, turns out to be very difficult to evolve. Often the system discovers mechanical strategies (such as moving back and forth) that help the agent cope, but are not very effective, do not appear believable and would not generalize to new environments. The problem is that a general strategy is too difficult for the evolution system to discover directly. This paper proposes an approach where such complex general behavior is learned incrementally, by starting with simpler behavior and gradually making the task more challenging and general. The task transitions are implemented through successive stages of delta-coding (i.e. evolving modifications), which allows even converged populations to adapt to the new task. The method is tested in the stochastic, dynamic task of prey capture, and compared with direct evolution. The incremental approach evolves more effective and more general behavior, and should also scale up to harder tasks.

Connected through hyperedge HE-1 (induced by source paper "2-D Pole Balancing with Recurrent Evolutionary Networks"):
  with members:
    - "Evolving Obstacle Avoidance Behavior in a Robot Arm"
    - "Machine Learning,  Efficient Reinforcement Learning through Symbiotic Evolution"

Connected through hyperedge HE-2 (induced by source paper "Real-time Interactive Neuro-evolution"):
  with members:
    - "Discovering Complex Othello Strategies Through Evolutionary Neural Networks"
    - "Machine Learning,  Efficient Reinforcement Learning through Symbiotic Evolution"
    - "Evolving Neural Networks to Play Go"

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: Reinforcement_Learning