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: "Team-Partitioned, Opaque-Transition Reinforcement Learning"
  Abstract: In this paper, we present a novel multi-agent learning paradigm called team-partitioned, opaque-transition reinforcement learning (TPOT-RL). TPOT-RL introduces the concept of using action-dependent features to generalize the state space. In our work, we use a learned action-dependent feature space. TPOT-RL is an effective technique to allow a team of agents to learn to cooperate towards the achievement of a specific goal. It is an adaptation of traditional RL methods that is applicable in complex, non-Markovian, multi-agent domains with large state spaces and limited training opportunities. Multi-agent scenarios are opaque-transition, as team members are not always in full communication with one another and adversaries may affect the environment. Hence, each learner cannot rely on having knowledge of future state transitions after acting in the world. TPOT-RL enables teams of agents to learn effective policies with very few training examples even in the face of a large state space with large amounts of hidden state. The main responsible features are: dividing the learning task among team members, using a very coarse, action-dependent feature space, and allowing agents to gather reinforcement directly from observation of the environment. TPOT-RL is fully implemented and has been tested in the robotic soccer domain, a complex, multi-agent framework. This paper presents the algorithmic details of TPOT-RL as well as empirical results demonstrating the effectiveness of the developed multi-agent learning approach with learned features.

Connected through hyperedge HE-1 (induced by source paper "INCREMENTAL SELF-IMPROVEMENT FOR LIFE-TIME MULTI-AGENT REINFORCEMENT LEARNING"):
  with members:
    (none)

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 in Multi-Robot Systems"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "Co-Evolving Soccer Softbot Team Coordination with Genetic Programming"):
  with members:
    - "Strongly Typed Genetic Programming"

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"
    - "Reinforcement Learning with Imitation in Heterogeneous Multi-Agent Systems"

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