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: "A Cognitive Model of Learning to Navigate"
  Abstract: Our goal is to develop a cognitive model of how humans acquire skills on complex cognitive tasks. We are pursuing this goal by designing computational architectures for the NRL Navigation task, which requires competent sensorimotor coordination. In this paper, we analyze the NRL Navigation task in depth. We then use data from experiments with human subjects learning this task to guide us in constructing a cognitive model of skill acquisition for the task. Verbal protocol data augments the black box view provided by execution traces of inputs and outputs. Computational experiments allow us to explore a space of alternative architectures for the task, guided by the quality of fit to human performance data.

Connected through hyperedge HE-1 (induced by source paper "Planning and Acting in Partially Observable Stochastic Domains"):
  with members:
    - "Optimal Navigation in a Probibalistic World"
    - "Active Gesture Recognition using Partially Observable Markov Decision Processes"

Connected through hyperedge HE-2 (induced by source paper "A Comparison of Action Selection Learning Methods"):
  with members:
    - "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"
    - "Forward models: Supervised learning with a distal teacher"

Connected through hyperedge HE-3 (induced by source paper "The Parti-game Algorithm for Variable Resolution Reinforcement Learning in Multidimensional State-spaces"):
  with members:
    - "Learning to Act using Real-Time Dynamic Programming"
    - "References elements that can solve difficult learning control problems. on Simulation of Adaptive Behavior, pages"
    - "Integrated Architectures for Learning, Planning, and Reacting Based on Approximating Dynamic Programming"
    - "On the Complexity of Solving Markov Decision Problems"
    - "Applying Online Search Techniques to Continuous-State Reinforcement Learning key to the success of the local"
    - "Machine Learning,  Explanation-Based Learning and Reinforcement Learning: A Unified View"
    - "Learning to Use Selective Attention and Short-Term Memory in Sequential Tasks"

Connected through hyperedge HE-4 (induced by source paper "Forward models: Supervised learning with a distal teacher"):
  with members:
    - "Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,"
    - "Book Review  Introduction to the Theory of Neural Computation Reviewed by: 2"
    - "References "Using Neural Networks to Identify Jets", Kohonen, "Self Organized Formation of Topologically Correct Feature"
    - "References elements that can solve difficult learning control problems. on Simulation of Adaptive Behavior, pages"
    - "Integrated Architectures for Learning, Planning, and Reacting Based on Approximating Dynamic Programming"
    - "Framework for Combining Symbolic and Neural Learning rule extraction from neural networks the KBANN algorithm"
    - "A Comparison of Action Selection Learning Methods"
    - "Computational Models of Sensorimotor Integration  Computational Maps and Motor Control."

Connected through hyperedge HE-5 (induced by source paper "Reinforcement Learning with Soft State Aggregation"):
  with members:
    - "Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,"
    - "References elements that can solve difficult learning control problems. on Simulation of Adaptive Behavior, pages"
    - "On the Convergence of Stochastic Iterative Dynamic Programming Algorithms"
    - "Learning Without State-Estimation in Partially Observable Markovian Decision Processes"

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