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 "An Upper Bound on the Loss from Approximate Optimal-Value Functions"):
  Abstract: Many reinforcement learning (RL) approaches can be formulated from the theory of Markov decision processes and the associated method of dynamic programming (DP). The value of this theoretical understanding, however, is tempered by many practical concerns. One important question is whether DP-based approaches that use function approximation rather than lookup tables, can avoid catastrophic effects on performance. This note presents a result in Bertsekas (1987) which guarantees that small errors in the approximation of a task's optimal value function cannot produce arbitrarily bad performance when actions are selected greedily. We derive an upper bound on performance loss which is slightly tighter than that in Bertsekas (1987), and we show the extension of the bound to Q-learning (Watkins, 1989). These results provide a theoretical justification for a practice that is common in reinforcement learning.

Includes members:
  Member "Learning to Act using Real-Time Dynamic Programming":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "On the Complexity of Solving Markov Decision Problems")
  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 "References elements that can solve difficult learning control problems. on Simulation of Adaptive Behavior, pages":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "LEARNING TO AVOID COLLISIONS: A REINFORCEMENT LEARNING PARADIGM FOR MOBILE ROBOT NAVIGATION")
  Member "Generalization in Reinforcement Learning: Safely Approximating the Value Function":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "A Reinforcement Learning Approach to Job-shop Scheduling")
  Member "Integrated Architectures for Learning, Planning, and Reacting Based on Approximating Dynamic Programming":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "LEARNING TO AVOID COLLISIONS: A REINFORCEMENT LEARNING PARADIGM FOR MOBILE ROBOT NAVIGATION")
  Member "Analysis of Some Incremental Variants of Policy Iteration: First Steps Toward Understanding Actor-Critic Learning Systems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "Asynchronous Modified Policy Iteration with Single-sided Updates")
  Member "Issues in Using Function Approximation for Reinforcement Learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "On the Convergence of Stochastic Iterative Dynamic Programming Algorithms")
  Member "Tight Performance Bounds on Greedy Policies Based on Imperfect Value Functions":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "On the Complexity of Solving Markov Decision Problems")

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