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: "Emergent Control and Planning in an Autonomous Vehicle"
  Abstract: We use a connectionist network trained with reinforcement to control both an autonomous robot vehicle and a simulated robot. We show that given appropriate sensory data and architectural structure, a network can learn to control the robot for a simple navigation problem. We then investigate a more complex goal-based problem and examine the plan-like behavior that emerges.

Connected through hyperedge HE-1 (induced by source paper "Generalization and scaling in reinforcement learning"):
  with members:
    - "Modeling the Evolution of Motivation"
    - "Adaptation in constant utility non-stationary environments"
    - "EVOLVING SENSORS IN ENVIRONMENTS OF CONTROLLED COMPLEXITY"

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