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: "Unsupervised Discrimination of Clustered Data via Optimization of Binary Information Gain"
  Abstract: We present the information-theoretic derivation of a learning algorithm that clusters unlabelled data with linear discriminants. In contrast to methods that try to preserve information about the input patterns, we maximize the information gained from observing the output of robust binary discriminators implemented with sigmoid nodes. We derive a local weight adaptation rule via gradient ascent in this objective, demonstrate its dynamics on some simple data sets, relate our approach to previous work and suggest directions in which it may be extended.

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 "On Centering Neural Network Weight Updates"):
  with members:
    - "Feature Extraction Using an Unsupervised Neural Network"
    - "Tempering Backpropagation Networks: Not All Weights are Created Equal approach yields hitherto unparalleled performance on"

Connected through hyperedge HE-3 (induced by source paper "Plasticity-Mediated Competitive Learning"):
  with members:
    - "LEARNING FACTORIAL CODES BY PREDICTABILITY MINIMIZATION (Neural Computation, 4(6):863-879, 1992)"
    - "A Non-linear Information Maximisation Algorithm that Performs Blind Separation."

Connected through hyperedge HE-4 (induced by source paper "Centering Neural Network Gradient Factors"):
  with members:
    - "Feature Extraction Using an Unsupervised Neural Network"
    - "Early Stopping but when?"
    - "Tempering Backpropagation Networks: Not All Weights are Created Equal approach yields hitherto unparalleled performance on"

Connected through hyperedge HE-5 (induced by source paper "Empirical Entropy Manipulation for Real-World Problems"):
  with members:
    - "An information-maximisation approach to blind separation and blind deconvolution"
    - "Objective Function Formulation of the BCM Theory of Visual Cortical Plasticity: Statistical Connections, Stability Conditions"

Connected through hyperedge HE-6 (induced by source paper "LEARNING FACTORIAL CODES BY PREDICTABILITY MINIMIZATION (Neural Computation, 4(6):863-879, 1992)"):
  with members:
    - "An information-maximisation approach to blind separation and blind deconvolution"
    - "LEARNING COMPLEX, EXTENDED SEQUENCES USING THE PRINCIPLE OF HISTORY COMPRESSION (Neural Computation, 4(2):234-242, 1992)"
    - "Unsupervised Neural Network Learning Procedures For Feature Extraction and Classification"
    - "Plasticity-Mediated Competitive Learning"
    - "SEMILINEAR PREDICTABILITY MINIMIZATION PRODUCES WELL-KNOWN FEATURE DETECTORS Neural Computation, 1996 (accepted)"

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