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: "BCM Network develops Orientation Selectivity and Ocular Dominance in Natural Scene Environment."
  Abstract: A two-eye visual environment is used in training a network of BCM neurons. We study the effect of misalignment between the synaptic density functions from the two eyes, on the formation of orientation selectivity and ocular dominance in a lateral inhibition network. The visual environment we use is composed of natural images. We show that for the BCM rule a natural image environment with binocular cortical misalignment is sufficient for producing networks with orientation selective cells and ocular dominance columns. This work is an extension of our previous single cell misalignment model (Shouval et al., 1996).

Connected through hyperedge HE-1 (induced by source paper "Finding Compact and Sparse Distributed Representations of Visual Images"):
  with members:
    - "Neuronal Goals: Efficient Coding and Coincidence Detection"

Connected through hyperedge HE-2 (induced by source paper "Natural image statistics and efficient coding"):
  with members:
    - "An information-maximisation approach to blind separation and blind deconvolution"
    - "Neuronal Goals: Efficient Coding and Coincidence Detection"

Connected through hyperedge HE-3 (induced by source paper "Objective Function Formulation of the BCM Theory of Visual Cortical Plasticity: Statistical Connections, Stability Conditions"):
  with members:
    - "Theory of Synaptic Plasticity in Visual Cortex"
    - "Feature Extraction Using an Unsupervised Neural Network"
    - "Multimodality Exploration in Training an Unsupervised Projection Pursuit Neural Network"
    - "An integrated approach to the study of object features in visual recognition"
    - "Three-Dimensional Object Recognition Using an Unsupervised BCM Network: The Usefulness of Distinguishing Features"
    - "3D Object Recognition Using Unsupervised Feature Extraction"
    - "Classification of Underwater Mammals using Feature Extraction Based on Time-Frequency Analysis and BCM Theory"
    - "Combining Exploratory Projection Pursuit And Projection Pursuit Regression With Application To Neural Networks"

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