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: "LEARNING TO GENERATE ARTIFICIAL FOVEA TRAJECTORIES FOR TARGET DETECTION"
  Abstract: It is shown how `static' neural approaches to adaptive target detection can be replaced by a more efficient and more sequential alternative. The latter is inspired by the observation that biological systems employ sequential eye-movements for pattern recognition. A system is described which builds an adaptive model of the time-varying inputs of an artificial fovea controlled by an adaptive neural controller. The controller uses the adaptive model for learning the sequential generation of fovea trajectories causing the fovea to move to a target in a visual scene. The system also learns to track moving targets. No teacher provides the desired activations of `eye-muscles' at various times. The only goal information is the shape of the target. Since the task is a `reward-only-at-goal' task , it involves a complex temporal credit assignment problem. Some implications for adaptive attentive systems in general are discussed.

Connected through hyperedge HE-1 (induced by source paper "Cholinergic suppression of transmission may allow combined associative memory function and self-organization in the neocortex."):
  with members:
    - "Multiassociative Memory"
    - "Convergence-Zone Episodic Memory: Analysis and Simulations"
    - "Topography And Ocular Dominance: A Model Exploring Positive Correlations"
    - "Self-Organization and Functional Role of Lateral Connections and Multisize Receptive Fields in the Primary Visual Cortex"
    - "Neural Network Applicability: Classifying the Problem Space"
    - "Competitive Anti-Hebbian Learning of Invariants"
    - "Multiple Network Systems (Minos) Modules: Task Division and Module Discrimination 1"
    - "Living in a partially structured environment: How to bypass the limitations of classical reinforcement techniques"

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