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: "A HIERARCHICAL COMMUNITY OF EXPERTS"
  Abstract: We describe a directed acyclic graphical model that contains a hierarchy of linear units and a mechanism for dynamically selecting an appropriate subset of these units to model each observation. The non-linear selection mechanism is a hierarchy of binary units each of which gates the output of one of the linear units. There are no connections from linear units to binary units, so the generative model can be viewed as a logistic belief net (Neal 1992) which selects a skeleton linear model from among the available linear units. We show that Gibbs sampling can be used to learn the parameters of the linear and binary units even when the sampling is so brief that the Markov chain is far from equilibrium.

Connected through hyperedge HE-1 (induced by source paper "The EM Algorithm for Mixtures of Factor Analyzers"):
  with members:
    - "Data Mining for Association Rules with Unsupervised Neural Networks"
    - "Data Mining for Association Rules with Unsupervised Neural Networks"
    - "EM Algorithms for PCA and SPCA"
    - "Recognizing Handwritten Digits Using Mixtures of Linear Models"

Connected through hyperedge HE-2 (induced by source paper "Generative Models for Discovering Sparse Distributed Representations"):
  with members:
    - "References "Using Neural Networks to Identify Jets", Kohonen, "Self Organized Formation of Topologically Correct Feature"
    - "Unsupervised Learning by Convex and Conic Coding"
    - "Pattern analysis and synthesis in attractor neural networks"
    - "The Rectified Gaussian Distribution"
    - "Data Mining for Association Rules with Unsupervised Neural Networks"
    - "Data Mining for Association Rules with Unsupervised Neural Networks"
    - "Continuous sigmoidal belief networks trained using slice sampling"
    - "Factor Analysis Using Delta-Rule Wake-Sleep Learning"

Connected through hyperedge HE-3 (induced by source paper "Hierarchical Mixtures of Experts and the EM Algorithm"):
  with members:
    - "Validation of Average Error Rate Over Classifiers"
    - "Comparison of Neural and Statistical Classifiers| Theory and Practice"
    - "A Modular Q-Learning Architecture for Manipulator Task Decomposition `Data storage in the cerebellar model ar"
    - "Supervised learning from incomplete data via an EM approach"
    - "A Statistical Approach to Decision Tree Modeling"
    - "On learning hierarchical classifications"
    - "Using Neural Networks for Descriptive Statistical Analysis of Educational Data"
    - "Factorial Learning and the EM Algorithm"

Connected through hyperedge HE-4 (induced by source paper "A VIEW OF THE EM ALGORITHM THAT JUSTIFIES INCREMENTAL, SPARSE, AND OTHER VARIANTS"):
  with members:
    - "Mean Field Theory for Sigmoid Belief Networks"
    - "A Statistical Approach to Decision Tree Modeling"
    - "On Convergence Properties of the EM Algorithm for Gaussian Mixtures"
    - "Efficient Stochastic Source Coding and an Application to a Bayesian Network Source Model"
    - "On Structured Variational Approximations"
    - "The Expectation-Maximization Algorithm for MAP Estimation"
    - "Localist Attractor Networks"
    - "Sequential Update of Bayesian Network Structure"

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