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: "PHONETIC CLASSIFICATION OF TIMIT SEGMENTS PREPROCESSED WITH LYON'S COCHLEAR MODEL USING A SUPERVISED/UNSUPERVISED HYBRID NEURAL NETWORK"
  Abstract: We report results on vowel and stop consonant recognition with tokens extracted from the TIMIT database. Our current system differs from others doing similar tasks in that we do not use any specific time normalization techniques. We use a very detailed biologically motivated input representation of the speech tokens - Lyon's cochlear model as implemented by Slaney [20]. This detailed, high dimensional representation, known as a cochleagram, is classified by either a back-propagation or by a hybrid supervised/unsupervised neural network classifier. The hybrid network is composed of a biologically motivated unsupervised network and a supervised back-propagation network. This approach produces results comparable to those obtained by others without the addition of time normalization.

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 "Combining Exploratory Projection Pursuit And Projection Pursuit Regression With Application To Neural Networks"):
  with members:
    - "Objective Function Formulation of the BCM Theory of Visual Cortical Plasticity: Statistical Connections, Stability Conditions"
    - "Feature Extraction Using an Unsupervised Neural Network"
    - "On the Combination of Supervised and Unsupervised Learning  reducing the overall error measure of a classifier."
    - "Face Recognition using a Hybrid Supervised/Unsupervised Neural Network"
    - "Extraction of Facial Features for Recognition using Neural Networks"

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"

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"
    - "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"
    - "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