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: "Reinforcement Learning with Imitation in Heterogeneous Multi-Agent Systems"
  Abstract: The application of decision making and learning algorithms to multi-agent systems presents many interestingresearch challenges and opportunities. Among these is the ability for agents to learn how to act by observing or imitating other agents. We describe an algorithm, the IQ-algorithm, that integrates imitation with Q-learning. Roughly, a Q-learner uses the observations it has made of an expert agent to bias its exploration in promising directions. This algorithm goes beyond previous work in this direction by relaxing the oft-made assumptions that the learner (observer) and the expert (observed agent) share the same objectives and abilities. Our preliminary experiments demonstrate significant transfer between agents using the IQ-model and in many cases reductions in training time.

Connected through hyperedge HE-1 (induced by source paper "Learning to coordinate without sharing information"):
  with members:
    - "Integrated Architectures for Learning, Planning, and Reacting Based on Approximating Dynamic Programming"
    - "Multi-Agent Reinforcement Learning: Independent vs. Cooperative Agents"
    - "Adaptive Load Balancing: A Study in Multi-Agent Learning"
    - "Figure 3: Average model size accepted from a ran-dom prefix-closed samples of various size, and"

Connected through hyperedge HE-2 (induced by source paper "Reinforcement Learning: A Survey"):
  with members:
    - "Multiagent Reinforcement Learning: Theoretical Framework and an Algorithm"
    - "Learning to Use Selective Attention and Short-Term Memory in Sequential Tasks"
    - "Adding Memory to XCS"

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"

Connected through hyperedge HE-4 (induced by source paper "Markov games as a framework for multi-agent reinforcement learning"):
  with members:
    - "Generalized Markov Decision Processes: Dynamic-programming and Reinforcement-learning Algorithms"
    - "A Competitive Approach to Game Learning"
    - "Learning Team Strategies With Multiple Policy-Sharing Agents: A Soccer Case Study"
    - "database"
    - "Multiagent Reinforcement Learning: Theoretical Framework and an Algorithm"
    - "Multi-Agent Reinforcement Learning: Independent vs. Cooperative Agents"
    - "Learning Conventions in Multiagent Stochastic Domains using Likelihood Estimates"
    - "Using Communication to Reduce Locality in Distributed Multi-Agent Learning"

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