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 "Value Function Approximations and Job-Shop Scheduling"):
  Abstract: We report a successful application of TD() with value function approximation to the task of job-shop scheduling. Our scheduling problems are based on the problem of scheduling payload processing steps for the NASA space shuttle program. The value function is approximated by a 2-layer feedforward network of sigmoid units. A one-step lookahead greedy algorithm using the learned evaluation function outperforms the best existing algorithm for this task, which is an iterative repair method incorporating simulated annealing. To understand the reasons for this performance improvement, this paper introduces several measurements of the learning process and discusses several hypotheses suggested by these measurements. We conclude that the use of value function approximation is not a source of difficulty for our method, and in fact, it may explain the success of the method independent of the use of value iteration. Additional experiments are required to discriminate among our hypotheses.

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 "Generalization in Reinforcement Learning: Safely Approximating the Value Function":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "A Reinforcement Learning Approach to Job-shop Scheduling")
  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")

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