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 "Near-Optimal Performance for Reinforcement Learning in Polynomial Time"):
  Abstract: We present new algorithms for reinforcement learning and prove that they have polynomial bounds on the resources required to achieve near-optimal return in general Markov decision processes. After observing that the number of actions required to approach the optimal return is lower bounded by the mixing time T of the optimal policy (in the undiscounted case) or by the horizon time T (in the discounted case), we then give algorithms requiring a number of actions and total computation time that are only polynomial in T and the number of states, for both the undiscounted and discounted cases. An interesting aspect of our algorithms is their explicit handling of the Exploration-Exploitation trade-off. These are the first results in the reinforcement learning literature giving algorithms that provably converge to near-optimal performance in polynomial time for general Markov decision processes.

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 "Learning Curve Bounds for Markov Decision Processes with Undiscounted Rewards":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Learning to Act using Real-Time Dynamic Programming")
  Member "Analytical Mean Squared Error Curves in Temporal Difference Learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Machine Learning,  Reinforcement Learning with Replacing Eligibility Traces")
  Member "On the Convergence of Stochastic Iterative Dynamic Programming Algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "Towards a Reactive Critic")
  Member "Machine Learning, 22(1/2/3):95-121, 1996. On the Worst-case Analysis of Temporal-difference Learning Algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "On the Convergence of Stochastic Iterative Dynamic Programming Algorithms")

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