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: "Feature Extraction Using an Unsupervised Neural Network"
  Abstract: A novel unsupervised neural network for dimensionality reduction that seeks directions emphasizing multimodality is presented, and its connection to exploratory projection pursuit methods is discussed. This leads to a new statistical insight into the synaptic modification equations governing learning in Bienenstock, Cooper, and Munro (BCM) neurons (1982). The importance of a dimensionality reduction principle based solely on distinguishing features is demonstrated using a phoneme recognition experiment. The extracted features are compared with features extracted using a back-propagation network.

Connected through hyperedge HE-1 (induced by source paper "Unsupervised Discrimination of Clustered Data via Optimization of Binary Information Gain"):
  with members:
    - "LEARNING FACTORIAL CODES BY PREDICTABILITY MINIMIZATION (Neural Computation, 4(6):863-879, 1992)"
    - "On Centering Neural Network Weight Updates"
    - "Centering Neural Network Gradient Factors"
    - "Empirical Entropy Manipulation for Real-World Problems"
    - "Plasticity-Mediated Competitive Learning"

Connected through hyperedge HE-2 (induced by source paper "On Centering Neural Network Weight Updates"):
  with members:
    - "Unsupervised Discrimination of Clustered Data via Optimization of Binary Information Gain"
    - "Tempering Backpropagation Networks: Not All Weights are Created Equal approach yields hitherto unparalleled performance on"

Connected through hyperedge HE-3 (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"
    - "Objective Function Formulation of the BCM Theory of Visual Cortical Plasticity: Statistical Connections, Stability Conditions"

Connected through hyperedge HE-4 (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"
    - "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-5 (induced by source paper "Classification of Underwater Mammals using Feature Extraction Based on Time-Frequency Analysis and BCM Theory"):
  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"
    - "Face Recognition using a Hybrid Supervised/Unsupervised Neural Network"

Connected through hyperedge HE-6 (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."
    - "Objective Function Formulation of the BCM Theory of Visual Cortical Plasticity: Statistical Connections, Stability Conditions"

Connected through hyperedge HE-7 (induced by source paper "3D Object Recognition Using Unsupervised Feature Extraction"):
  with members:
    - "Objective Function Formulation of the BCM Theory of Visual Cortical Plasticity: Statistical Connections, Stability Conditions"

Connected through hyperedge HE-8 (induced by source paper "Centering Neural Network Gradient Factors"):
  with members:
    - "Unsupervised Discrimination of Clustered Data via Optimization of Binary Information Gain"
    - "Early Stopping but when?"
    - "Tempering Backpropagation Networks: Not All Weights are Created Equal approach yields hitherto unparalleled performance on"

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