human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge groups papers that share a common citing paper.

Details of the hypergraph:
Center hyperedge (induced by source paper "Transfer of Learning by Composing Solutions of Elemental Sequential Tasks"):
  Abstract: Although building sophisticated learning agents that operate in complex environments will require learning to perform multiple tasks, most applications of reinforcement learning have focussed on single tasks. In this paper I consider a class of sequential decision tasks (SDTs), called composite sequential decision tasks, formed by temporally concatenating a number of elemental sequential decision tasks. Elemental SDTs cannot be decomposed into simpler SDTs. I consider a learning agent that has to learn to solve a set of elemental and composite SDTs. I assume that the structure of the composite tasks is unknown to the learning agent. The straightforward application of reinforcement learning to multiple tasks requires learning the tasks separately, which can waste computational resources, both memory and time. I present a new learning algorithm and a modular architecture that learns the decomposition of composite SDTs, and achieves transfer of learning by sharing the solutions of elemental SDTs across multiple composite SDTs. The solution of a composite SDT is constructed by computationally inexpensive modifications of the solutions of its constituent elemental SDTs. I provide a proof of one aspect of the learning algorithm.

Includes members:
  Member "The Efficient Learning of Multiple Task Sequences":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Learning to Act using Real-Time Dynamic Programming")
  Member "A Modular Q-Learning Architecture for Manipulator Task Decomposition `Data storage in the cerebellar model ar":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "The Efficient Learning of Multiple Task Sequences")
  Member "A Coevolutionary Approach to Learning Sequential Decision Rules":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Learning Sequential Decision Rules Using Simulation Models and Competition")
  Member "Hierarchical Explanation-Based Reinforcement Learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "Machine Learning,  Explanation-Based Learning and Reinforcement Learning: A Unified View")
  Member "Robust Reinforcement Learning in Motion Planning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "Learning to Act using Real-Time Dynamic Programming")
  Member "Learning to Achieve Goals":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "Learning to Act using Real-Time Dynamic Programming")
  Member "Using Case-Based Reasoning as a Reinforcement Learning Framework for Optimization with Changing Criteria":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,")
  Member "Emergent Hierarchical Control Structures: Learning Reactive/Hierarchical Relationships in Reinforcement Environments":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "Learning Hierarchical Control Structures for Multiple Tasks and Changing Environments")

Question: Please tell me which class the citing paper (i.e., the source paper of the center hyperedge) 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