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: "Residual Q-Learning Applied to Visual Attention"
  Abstract: Foveal vision features imagers with graded acuity coupled with context sensitive sensor gaze control, analogous to that prevalent throughout vertebrate vision. Foveal vision operates more efficiently than uniform acuity vision because resolution is treated as a dynamically allocatable resource, but requires a more refined visual attention mechanism. We demonstrate that reinforcement learning (RL) significantly improves the performance of foveal visual attention, and of the overall vision system, for the task of model based target recognition. A simulated foveal vision system is shown to classify targets with fewer fixations by learning strategies for the acquisition of visual information relevant to the task, and learning how to generalize these strategies in ambiguous and unexpected scenario conditions.

Connected through hyperedge HE-1 (induced by source paper "MultiPlayer Residual Advantage Learning With General Function Approximation"):
  with members:
    - "Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,"
    - "Generalization in Reinforcement Learning: Safely Approximating the Value Function"
    - "Generalized Markov Decision Processes: Dynamic-programming and Reinforcement-learning Algorithms"
    - "Adapting Bias by Gradient Descent: An Incremental Version of Delta-Bar-Delta"
    - "Spurious Solutions to the Bellman Equation"
    - "Metrics for Temporal Difference 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