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: "Objective Function Formulation of the BCM Theory of Visual Cortical Plasticity: Statistical Connections, Stability Conditions"
  Abstract: In this paper, we present an objective function formulation of the BCM theory of visual cortical plasticity that permits us to demonstrate the connection between the unsupervised BCM learning procedure and various statistical methods, in particular, that of Projection Pursuit. This formulation provides a general method for stability analysis of the fixed points of the theory and enables us to analyze the behavior and the evolution of the network under various visual rearing conditions. It also allows comparison with many existing unsupervised methods. This model has been shown successful in various applications such as phoneme and 3D object recognition. We thus have the striking and possibly highly significant result that a biological neuron is performing a sophisticated statistical procedure.

Connected through hyperedge HE-1 (induced by source paper "Receptive Fields for Vision: from Hyperacuity to Object Recognition"):
  with members:
    - "Learning networks for face analysis and synthesis"
    - "Models of perceptual learning in vernier hyperacuity"

Connected through hyperedge HE-2 (induced by source paper "Feature Extraction Using an Unsupervised Neural Network"):
  with members:
    - "Theory of Synaptic Plasticity in Visual Cortex"
    - "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-3 (induced by source paper "Observations on Cortical Mechanisms for Object Recognition and Learning"):
  with members:
    - "Learning networks for face analysis and synthesis"

Connected through hyperedge HE-4 (induced by source paper "Three-Dimensional Object Recognition Using an Unsupervised BCM Network: The Usefulness of Distinguishing Features"):
  with members:
    - "Learning networks for face analysis and synthesis"
    - "Feature Extraction Using an Unsupervised Neural Network"

Connected through hyperedge HE-5 (induced by source paper "Multimodality Exploration in Training an Unsupervised Projection Pursuit Neural Network"):
  with members:
    - "Feature Extraction Using an Unsupervised Neural Network"
    - "Classification of Underwater Mammals using Feature Extraction Based on Time-Frequency Analysis and BCM Theory"
    - "Face Recognition using a Hybrid Supervised/Unsupervised Neural Network"

Connected through hyperedge HE-6 (induced by source paper "Classification of Underwater Mammals using Feature Extraction Based on Time-Frequency Analysis and BCM Theory"):
  with members:
    - "Feature Extraction Using an Unsupervised Neural Network"
    - "Multimodality Exploration in Training an Unsupervised Projection Pursuit Neural Network"
    - "Face Recognition using a Hybrid Supervised/Unsupervised Neural Network"

Connected through hyperedge HE-7 (induced by source paper "Extraction of Facial Features for Recognition using Neural Networks"):
  with members:
    - "Ensemble Training: Some Recent Experiments with Postal Zip Data"
    - "Combining Exploratory Projection Pursuit And Projection Pursuit Regression With Application To Neural Networks"

Connected through hyperedge HE-8 (induced by source paper "Theory of Synaptic Plasticity in Visual Cortex"):
  with members:
    - "Cholinergic suppression of transmission may allow combined associative memory function and self-organization in the neocortex."
    - "Feature Extraction Using an Unsupervised 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