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: "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.

Connected through hyperedge HE-1 (induced by source paper "Robust Reinforcement Learning in Motion Planning"):
  with members:
    - "Learning to Act using Real-Time Dynamic Programming"

Connected through hyperedge HE-2 (induced by source paper "Learning to Achieve Goals"):
  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,"
    - "Integrated Architectures for Learning, Planning, and Reacting Based on Approximating Dynamic Programming"
    - "Generalized Markov Decision Processes: Dynamic-programming and Reinforcement-learning Algorithms"
    - "Finding Promising Exploration Regions by Weighting Expected Navigation Costs continuous environments, some first-order approximations to"

Connected through hyperedge HE-3 (induced by source paper "Learning Hierarchical Control Structures for Multiple Tasks and Changing Environments"):
  with members:
    - "Emergent Hierarchical Control Structures: Learning Reactive/Hierarchical Relationships in Reinforcement Environments"
    - "Learning in Continuous Domains with Delayed Rewards"

Connected through hyperedge HE-4 (induced by source paper "A Modular Q-Learning Architecture for Manipulator Task Decomposition `Data storage in the cerebellar model ar"):
  with members:
    - "Hierarchical Mixtures of Experts and the EM Algorithm"
    - "References elements that can solve difficult learning control problems. on Simulation of Adaptive Behavior, pages"
    - "The Efficient Learning of Multiple Task Sequences"
    - "Exploration and Model Building in Mobile Robot Domains"

Connected through hyperedge HE-5 (induced by source paper "Hierarchical Explanation-Based Reinforcement Learning"):
  with members:
    - "Machine Learning,  Explanation-Based Learning and Reinforcement Learning: A Unified View"
    - "Acquiring Recursive and Iterative Concepts with Explanation-Based Learning explanation-based generalization, generalizing explanation structures, generalizing to"

Connected through hyperedge HE-6 (induced by source paper "Using Case-Based Reasoning as a Reinforcement Learning Framework for Optimization with Changing Criteria"):
  with members:
    - "Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,"
    - "Using Case-Based Reasoning to Acquire User Scheduling Preferences that Change over Time"
    - "Case-based Acquisition of User Preferences for Solution Improvement in Ill-Structured Domains"

Connected through hyperedge HE-7 (induced by source paper "Hierarchical Reinforcement Learning with the MAXQ Value Function Decomposition"):
  with members:
    - "On the Convergence of Stochastic Iterative Dynamic Programming Algorithms"
    - "Reinforcement Learning with Hierarchies of Machines"
    - "Between MDPs and Semi-MDPs: Learning, Planning, and Representing Knowledge at Multiple Temporal Scales"

Connected through hyperedge HE-8 (induced by source paper "The Functional Transfer of Knowledge for Coronary Artery Disease Diagnosis"):
  with members:
    - "Learning Sequential Tasks by Incrementally Adding Higher Orders"
    - "Learning One More Thing"
    - "The Task Rehearsal Method of Sequential 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