human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent groups of papers sharing a common citing paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "On the Convergence of Stochastic Iterative Dynamic Programming Algorithms"):
  with members:
    - "Learning to Act using Real-Time Dynamic Programming"
    - "Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,"
    - "A Unified Analysis of Value-Function-Based Reinforcement-Learning Algorithms"
    - "Issues in Using Function Approximation for Reinforcement Learning"
    - "Reinforcement Learning with Soft State Aggregation"
    - "Reinforcement Learning Methods for Continuous-Time Markov Decision Problems"
    - "Reinforcement Learning Algorithm for Partially Observable Markov Decision Problems"
    - "Hierarchical Reinforcement Learning with the MAXQ Value Function Decomposition"

Connected through hyperedge HE-2 (induced by source paper "Learning Curve Bounds for Markov Decision Processes with Undiscounted Rewards"):
  with members:
    - "Learning to Act using Real-Time Dynamic Programming"
    - "Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,"
    - "Rigorous Learning Curve Bounds from Statistical Mechanics"
    - "Markov Decision Processes in Large State Spaces"
    - "Reinforcement Learning Algorithms for Average-Payoff Markovian Decision Processes"

Connected through hyperedge HE-3 (induced by source paper "Analytical Mean Squared Error Curves in Temporal Difference Learning"):
  with members:
    - "Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,"
    - "Machine Learning,  Reinforcement Learning with Replacing Eligibility Traces"

Connected through hyperedge HE-4 (induced by source paper "Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,"):
  with members:
    - "Submitted to NIPS96, Section: Applications. Preference: Oral presentation Reinforcement Learning for Dynamic Channel Allocation in"
    - "High-Performance Job-Shop Scheduling With A Time-Delay TD() Network"
    - "Active Gesture Recognition using Partially Observable Markov Decision Processes"
    - "References elements that can solve difficult learning control problems. on Simulation of Adaptive Behavior, pages"
    - "Generalization in Reinforcement Learning: Safely Approximating the Value Function"
    - "Using a Case Base of Surfaces to Speed-Up Reinforcement Learning"
    - "Integrated Architectures for Learning, Planning, and Reacting Based on Approximating Dynamic Programming"
    - "A Reinforcement Learning Approach to Job-shop Scheduling"

Connected through hyperedge HE-5 (induced by source paper "Machine Learning, 22(1/2/3):95-121, 1996. On the Worst-case Analysis of Temporal-difference Learning Algorithms"):
  with members:
    - "Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,"
    - "On the Convergence of Stochastic Iterative Dynamic Programming Algorithms"

Question: Please tell me which class the center node 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