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: "Development of triadic neural circuits for visual image stabilization under eye movements"
  Abstract: Human visual systems maintain a stable internal representation of a scene even though the image on the retina is constantly changing because of eye movements. Such stabilization can theoretically be effected by dynamic shifts in the receptive field (RF) of neurons in the visual system. This paper examines how a neural circuit can learn to generate such shifts. The shifts are controlled by eye position signals and compensate for the movement in the retinal image caused by eye movements. The development of a neural shifter circuit (Olshausen, Anderson, & Van Essen, 1992) is modeled using triadic connections. These connections are gated by signals that indicate the direction of gaze (eye position signals). In simulations, a neural model is exposed to sequences of stimuli paired with appropriate eye position signals. The initially

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: Neural_Networks