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: "Theory of Synaptic Plasticity in Visual Cortex"

Connected through hyperedge HE-1 (induced by source paper "Feature Extraction Using an Unsupervised Neural Network"):
  with members:
    - "Objective Function Formulation of the BCM Theory of Visual Cortical Plasticity: Statistical Connections, Stability Conditions"
    - "Multimodality Exploration in Training an Unsupervised Projection Pursuit Neural Network"
    - "An integrated approach to the study of object features in visual recognition"
    - "On the Combination of Supervised and Unsupervised Learning  reducing the overall error measure of a classifier."
    - "Three-Dimensional Object Recognition Using an Unsupervised BCM Network: The Usefulness of Distinguishing Features"
    - "3D Object Recognition Using Unsupervised Feature Extraction"
    - "On Centering Neural Network Weight Updates"
    - "Classification of Underwater Mammals using Feature Extraction Based on Time-Frequency Analysis and BCM Theory"

Connected through hyperedge HE-2 (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"

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:
    - "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"
    - "PHONETIC CLASSIFICATION OF TIMIT SEGMENTS PREPROCESSED WITH LYON'S COCHLEAR MODEL USING A SUPERVISED/UNSUPERVISED HYBRID NEURAL NETWORK"

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