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: "Generative Models for Discovering Sparse Distributed Representations"
  Abstract: We describe a hierarchical, generative model that can be viewed as a non-linear generalization of factor analysis and can be implemented in a neural network. The model uses bottom-up, top-down and lateral connections to perform Bayesian perceptual inference correctly. Once perceptual inference has been performed the connection strengths can be updated using a very simple learning rule that only requires locally available information. We demon strate that the network learns to extract sparse, distributed, hierarchical representations.

Connected through hyperedge HE-1 (induced by source paper "Continuous sigmoidal belief networks trained using slice sampling"):
  with members:
    - "Markov Chain Monte Carlo Methods Based on `Slicing' the Density Function"
    - "Discovering Structure in Continuous Variables Using Bayesian Networks"

Connected through hyperedge HE-2 (induced by source paper "Data Mining for Association Rules with Unsupervised Neural Networks"):
  with members:
    - "Recognizing Handwritten Digits Using Mixtures of Linear Models"
    - "The EM Algorithm for Mixtures of Factor Analyzers"

Connected through hyperedge HE-3 (induced by source paper "A HIERARCHICAL COMMUNITY OF EXPERTS"):
  with members:
    - "Hierarchical Mixtures of Experts and the EM Algorithm"
    - "A VIEW OF THE EM ALGORITHM THAT JUSTIFIES INCREMENTAL, SPARSE, AND OTHER VARIANTS"
    - "The EM Algorithm for Mixtures of Factor Analyzers"

Connected through hyperedge HE-4 (induced by source paper "The Rectified Gaussian Distribution"):
  with members:
    - "Representation of spatial orientation by the intrinsic dynamics of the head-direction cell ensemble: A theory"

Connected through hyperedge HE-5 (induced by source paper "Pattern analysis and synthesis in attractor neural networks"):
  with members:
    - "Learning Generative Models with the Up-Propagation Algorithm"
    - "Unsupervised Learning by Convex and Conic Coding"
    - "Learning Continuous Attractors in Recurrent Networks"

Connected through hyperedge HE-6 (induced by source paper "Unsupervised Learning by Convex and Conic Coding"):
  with members:
    - "Learning Generative Models with the Up-Propagation Algorithm"
    - "Pattern analysis and synthesis in attractor neural networks"
    - "Factorial Learning and the EM Algorithm"
    - "Unsupervised learning of distributions on binary vectors using two layer networks"
    - "Extracting Support Data for a Given Task"

Connected through hyperedge HE-7 (induced by source paper "Factor Analysis Using Delta-Rule Wake-Sleep Learning"):
  with members:
    - "Recognizing Handwritten Digits Using Mixtures of Linear Models"

Connected through hyperedge HE-8 (induced by source paper "References "Using Neural Networks to Identify Jets", Kohonen, "Self Organized Formation of Topologically Correct Feature"):
  with members:
    - "Topography And Ocular Dominance: A Model Exploring Positive Correlations"
    - "Constrained Optimization for Neural Map Formation: A Unifying Framework for Weight Growth and Normalization"
    - "Self-Organization and Functional Role of Lateral Connections and Multisize Receptive Fields in the Primary Visual Cortex"
    - "SCRIPT RECOGNITION WITH HIERARCHICAL FEATURE MAPS"
    - "SELF-ORGANIZING PROCESS BASED ON LATERAL INHIBITION AND SYNAPTIC RESOURCE REDISTRIBUTION"
    - "IEEE Learning the Semantic Similarity of Reusable Software Components"
    - "Self-Organization and Segmentation in a Laterally Connected Orientation Map of Spiking Neurons"
    - "Soft Computing: the Convergence of Emerging Reasoning Technologies"

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