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: "Multi-criteria reinforcement learning"
  Abstract: We consider multi-criteria sequential decision making problems, where the criteria are ordered according to their importance. Structural properties of these problems are touched and reinforcement learning algorithms, which learn asymptotically optimal decisions, are derived. Computer experiments confirm the theoretical results and provide further insight in the learning processes.

Connected through hyperedge HE-1 (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-2 (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"
    - "On the Convergence of Stochastic Iterative Dynamic Programming Algorithms"

Connected through hyperedge HE-3 (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