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: "On the Convergence of Stochastic Iterative Dynamic Programming Algorithms"
  Abstract: Recent developments in the area of reinforcement learning have yielded a number of new algorithms for the prediction and control of Markovian environments. These algorithms, including the TD() algorithm of Sutton (1988) and the Q-learning algorithm of Watkins (1989), can be motivated heuristically as approximations to dynamic programming (DP). In this paper we provide a rigorous proof of convergence of these DP-based learning algorithms by relating them to the powerful techniques of stochastic approximation theory via a new convergence theorem. The theorem establishes a general class of convergent algorithms to which both TD() and Q-learning belong. This report describes research done at the Dept. of Brain and Cognitive Sciences, the Center for Biological and Computational Learning, and the Artificial Intelligence Laboratory of the Massachusetts Institute of Technology. Support for CBCL is provided in part by a grant from the NSF (ASC-9217041). Support for the laboratory's artificial intelligence research is provided in part by the Advanced Research Projects Agency of the Dept. of Defense. The authors were supported by a grant from the McDonnell-Pew Foundation, by a grant from ATR Human Information Processing Research Laboratories, by a grant from Siemens Corporation, by by grant IRI-9013991 from the National Science Foundation, by grant N00014-90-J-1942 from the Office of Naval Research, and by NSF grant ECS-9216531 to support an Initiative in Intelligent Control at MIT. Michael I. Jordan is a NSF Presidential Young Investigator.

Connected through hyperedge HE-1 (induced by source paper "Towards a Reactive Critic"):
  with members:
    - "Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,"
    - "Truncating Temporal Differences: On the Efficient Implementation of TD() for Reinforcement Learning"

Connected through hyperedge HE-2 (induced by source paper "Learning to Act using Real-Time Dynamic Programming"):
  with members:
    - "Submitted to NIPS96, Section: Applications. Preference: Oral presentation Reinforcement Learning for Dynamic Channel Allocation in"
    - "Active Gesture Recognition using Partially Observable Markov Decision Processes"
    - "Scaling Up Average Reward Reinforcement Learning by Approximating the Domain Models and the Value Function"
    - "Learning sorting and decision trees with POMDPs"
    - "Markov Decision Processes in Large State Spaces"
    - "Learning Curve Bounds for Markov Decision Processes with Undiscounted Rewards"
    - "The Efficient Learning of Multiple Task Sequences"
    - "Exploration and Model Building in Mobile Robot Domains"

Connected through hyperedge HE-3 (induced by source paper "A Unified Analysis of Value-Function-Based Reinforcement-Learning Algorithms"):
  with members:
    - "Learning to Act using Real-Time Dynamic Programming"
    - "Generalized Markov Decision Processes: Dynamic-programming and Reinforcement-learning Algorithms"
    - "Machine Learning,  Reinforcement Learning with Replacing Eligibility Traces"
    - "Multiagent Reinforcement Learning: Theoretical Framework and an Algorithm"
    - "Multi-criteria reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "Q-Learning for Bandit Problems"):
  with members:
    - "Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,"
    - "Exploration Bonuses and Dual Control"

Connected through hyperedge HE-5 (induced by source paper "Hierarchical Reinforcement Learning with the MAXQ Value Function Decomposition"):
  with members:
    - "Transfer of Learning by Composing Solutions of Elemental Sequential Tasks"
    - "Reinforcement Learning with Hierarchies of Machines"
    - "Between MDPs and Semi-MDPs: Learning, Planning, and Representing Knowledge at Multiple Temporal Scales"

Connected through hyperedge HE-6 (induced by source paper "Issues in Using Function Approximation for Reinforcement Learning"):
  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,"
    - "Generalization in Reinforcement Learning: Safely Approximating the Value Function"
    - "An Upper Bound on the Loss from Approximate Optimal-Value Functions"
    - "Tight Performance Bounds on Greedy Policies Based on Imperfect Value Functions"
    - "Learning To Play the Game of Chess"
    - "Locally Weighted Learning for Control"

Connected through hyperedge HE-7 (induced by source paper "Reinforcement Learning Methods for Continuous-Time Markov Decision Problems"):
  with members:
    - "Learning to Act using Real-Time Dynamic Programming"
    - "In  Improving Elevator Performance Using Reinforcement Learning"
    - "Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,"
    - "Self-Improving Factory Simulation using Continuous-time Average-Reward Reinforcement Learning"

Connected through hyperedge HE-8 (induced by source paper "Near-Optimal Performance for Reinforcement Learning in Polynomial Time"):
  with members:
    - "Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,"
    - "Learning Curve Bounds for Markov Decision Processes with Undiscounted Rewards"
    - "Analytical Mean Squared Error Curves in Temporal Difference Learning"
    - "Machine Learning, 22(1/2/3):95-121, 1996. On the Worst-case Analysis of Temporal-difference Learning 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