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: "Learning in Multi-Robot Systems"
  Abstract: This paper 1 discusses why traditional reinforcement learning methods, and algorithms applied to those models, result in poor performance in dynamic, situated multi-agent domains characterized by multiple goals, noisy perception and action, and inconsistent reinforcement. We propose a methodology for designing the representation and the forcement functions that take advantage of implicit domain knowledge in order to accelerate learning in such domains, and demonstrate it experimentally in two different mobile robot domains.

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"
    - "Near-Optimal Performance for Reinforcement Learning in Polynomial Time"
    - "Reinforcement Learning Methods for Continuous-Time Markov Decision Problems"
    - "Reinforcement Learning Algorithm for Partially Observable Markov Decision Problems"

Connected through hyperedge HE-2 (induced by source paper "Multi-Agent Reinforcement Learning: Independent vs. Cooperative Agents"):
  with members:
    - "Markov games as a framework for multi-agent reinforcement learning"
    - "Multiagent Reinforcement Learning: Theoretical Framework and an Algorithm"
    - "Reinforcement Learning in the Multi-Robot Domain"
    - "Learning to coordinate without sharing information"
    - "Using Communication to Reduce Locality in Distributed Multi-Agent Learning"
    - "Adaptive Load Balancing: A Study in Multi-Agent Learning"
    - "Team-Partitioned, Opaque-Transition Reinforcement Learning"

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