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: "Classification of Underwater Mammals using Feature Extraction Based on Time-Frequency Analysis and BCM Theory"
  Abstract: Underwater mammal sound classification is demonstrated using a novel application of wavelet time/frequency decomposition and feature extraction using a BCM unsupervised network. Different feature extraction methods and different wavelet representations are studied. The system achieves outstanding classification performance even when tested with mammal sounds recorded at very different locations (from those used for training). The improved results suggest that nonlinear feature extraction from wavelet representations outperforms different linear choices of basis functions.

Connected through hyperedge HE-1 (induced by source paper "Feature Extraction Using an Unsupervised Neural Network"):
  with members:
    - "Theory of Synaptic Plasticity in Visual Cortex"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "Multimodality Exploration in Training an Unsupervised Projection Pursuit Neural Network"):
  with members:
    - "Objective Function Formulation of the BCM Theory of Visual Cortical Plasticity: Statistical Connections, Stability Conditions"
    - "Feature Extraction Using an Unsupervised Neural Network"
    - "Face Recognition using a Hybrid Supervised/Unsupervised Neural Network"

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"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "Face Recognition using a Hybrid Supervised/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"
    - "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"
    - "Neuronal Goals: Efficient Coding and Coincidence Detection"

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