human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge groups papers that share a common citing paper.

Details of the hypergraph:
Center hyperedge (induced by source paper "Coordinating Reactive Behaviors  keywords: reactive systems, planning and learning"):
  Abstract: Combinating reactivity with planning has been proposed as a means of compensating for potentially slow response times of planners while still making progress toward long term goals. The demands of rapid response and the complexity of many environments make it difficult to decompose, tune and coordinate reactive behaviors while ensuring consistency. Neural networks can address the tuning problem, but are less useful for decomposition and coordination. We hypothesize that interacting reactions can be decomposed into separate behaviors resident in separate networks and that the interaction can be coordinated through the tuning mechanism and a higher level controller. To explore these issues, we have implemented a neural network architecture as the reactive component of a two layer control system for a simulated race car. By varying the architecture, we test whether decomposing reactivity into separate behaviors leads to superior overall performance, coordination and learning convergence.

Includes members:
  Member "Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "TDLeaf(): Combining Temporal Difference learning with game-tree search.")
  Member "Strategy Learning with Multilayer Connectionist Representations 1":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Reinforcement Learning with Modular Neural Networks for Control")
  Member "Robot Shaping: Developing Situated Agents through Learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Learning to Act using Real-Time Dynamic Programming")
  Member "Framework for Combining Symbolic and Neural Learning rule extraction from neural networks the KBANN algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "Symbolic and Subsymbolic Learning for Vision: Some Possibilities")

Question: Please tell me which class the citing paper (i.e., the source paper of the center hyperedge) 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