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 "High-Performance Job-Shop Scheduling With A Time-Delay TD() Network"):
  Abstract: Job-shop scheduling is an important task for manufacturing industries. We are interested in the particular task of scheduling payload processing for NASA's space shuttle program. This paper summarizes our previous work on formulating this task for solution by the reinforcement learning algorithm T D(). A shortcoming of this previous work was its reliance on hand-engineered input features. This paper shows how to extend the time-delay neural network (TDNN) architecture to apply it to irregular-length schedules. Experimental tests show that this TDNN-T D() network can match the performance of our previous hand-engineered system. The tests also show that both neural network approaches significantly outperform the best previous (non-learning) solution to this problem in terms of the quality of the resulting schedules and the number of search steps required to construct them.

Includes members:
  Member "Submitted to NIPS96, Section: Applications. Preference: Oral presentation Reinforcement Learning for Dynamic Channel Allocation in":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Learning to Act using Real-Time Dynamic Programming")
  Member "Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "TDLeaf(): Combining Temporal Difference learning with game-tree search.")
  Member "A Reinforcement Learning Approach to Job-shop Scheduling":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Value Function Based Production Scheduling")
  Member "Solving Combinatorial Optimization Tasks by Reinforcement Learning: A General Methodology Applied to Resource-Constrained Scheduling":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "A Reinforcement Learning Approach to Job-shop Scheduling")
  Member "How to Dynamically Merge Markov Decision Processes":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "Learning to Act using Real-Time Dynamic Programming")

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