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: "Planning by Incremental Dynamic Programming"
  Abstract: This paper presents the basic results and ideas of dynamic programming as they relate most directly to the concerns of planning in AI. These form the theoretical basis for the incremental planning methods used in the integrated architecture Dyna. These incremental planning methods are based on continually updating an evaluation function and the situation-action mapping of a reactive system. Actions are generated by the reactive system and thus involve minimal delay, while the incremental planning process guarantees that the actions and evaluation function will eventually be optimal|no matter how extensive a search is required. These methods are well suited to stochastic tasks and to tasks in which a complete and accurate model is not available. For tasks too large to implement the situation-action mapping as a table, supervised-learning methods must be used, and their capabilities remain a significant limitation of the approach.

Connected through hyperedge HE-1 (induced by source paper "Learning Optimal Dialogue Strategies: A Case Study of a Spoken Dialogue Agent for Email"):
  with members:
    - "Learning to Act using Real-Time Dynamic Programming"

Connected through hyperedge HE-2 (induced by source paper "Integrated Architectures for Learning, Planning, and Reacting Based on Approximating Dynamic Programming"):
  with members:
    - "Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,"
    - "References elements that can solve difficult learning control problems. on Simulation of Adaptive Behavior, pages"
    - "Using a Case Base of Surfaces to Speed-Up Reinforcement Learning"
    - "Generalized Markov Decision Processes: Dynamic-programming and Reinforcement-learning Algorithms"
    - "Exploration and Model Building in Mobile Robot Domains"
    - "Strategy Learning with Multilayer Connectionist Representations 1"
    - "Least-Squares Temporal Difference Learning"
    - "An Upper Bound on the Loss from Approximate Optimal-Value Functions"

Connected through hyperedge HE-3 (induced by source paper "Tight Performance Bounds on Greedy Policies Based on Imperfect Value Functions"):
  with members:
    - "Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,"
    - "Generalization in Reinforcement Learning: Safely Approximating the Value Function"
    - "Integrated Architectures for Learning, Planning, and Reacting Based on Approximating Dynamic Programming"
    - "An Upper Bound on the Loss from Approximate Optimal-Value Functions"
    - "Issues in Using Function Approximation for Reinforcement Learning"
    - "On the Complexity of Solving Markov Decision Problems"
    - "Generalized Prioritized Sweeping"

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"

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