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 learning of distributions on binary vectors using two layer networks"
  Abstract: We present a distribution model for binary vectors, called the influence combination model and show how this model can be used as the basis for unsupervised learning algorithms for feature selection. The model is closely related to the Harmonium model defined by Smolensky [RM86][Ch.6]. In the first part of the paper we analyze properties of this distribution representation scheme. We show that arbitrary distributions of binary vectors can be approximated by the combination model. We show how the weight vectors in the model can be interpreted as high order correlation patterns among the input bits. We compare the combination model with the mixture model and with principle component analysis. In the second part of the paper we present two algorithms for learning the combination model from examples. The first algorithm is based on gradient ascent. Here we give a closed form for this gradient that is significantly easier to compute than the corresponding gradient for the general Boltzmann machine. The second learning algorithm is a greedy method that creates the hidden units and computes their weights one at a time. This method is a variant of projection pursuit density estimation. In the third part of the paper we give experimental results for these learning methods on synthetic data and on natural data of handwritten digit images.

Connected through hyperedge HE-1 (induced by source paper "Learning Stochastic Feedforward Networks"):
  with members:
    (none)

Connected through hyperedge HE-2 (induced by source paper "Learning in Boltzmann Trees"):
  with members:
    - "Exploiting Tractable Substructures in Intractable Networks"
    - "Boltzmann Machine learning using mean field theory and linear response correction"
    - "Boltzmann Chains and Hidden Markov Models"
    - "Decimatable Boltzmann Machines vs. Gibbs Sampling"

Connected through hyperedge HE-3 (induced by source paper "Mapping Bayesian Networks to Boltzmann Machines"):
  with members:
    - "Using Bayesian networks for incorporating probabilistic a priori knowledge into Boltzmann machines"
    - "Constructing Computationally Efficient Bayesian Models via Unsupervised Clustering  Probabilistic Reasoning and Bayesian Belief Networks,"

Connected through hyperedge HE-4 (induced by source paper "Book Review  Introduction to the Theory of Neural Computation Reviewed by: 2"):
  with members:
    - "Representing and Learning Visual Schemas in Neural Networks for Scene Analysis"
    - "Convergence-Zone Episodic Memory: Analysis and Simulations"
    - "Topography And Ocular Dominance: A Model Exploring Positive Correlations"
    - "Mean Field Theory for Sigmoid Belief Networks"
    - "Dyslexic and Category-Specific Aphasic Impairments in a Self-Organizing Feature Map Model of the Lexicon"
    - "Neural Networks and Statistical Models Proceedings of the Nineteenth Annual SAS Users Group International Conference,"
    - "Boltzmann Machine learning using mean field theory and linear response correction"
    - "Induction of Multiscale Temporal Structure"

Connected through hyperedge HE-5 (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"
    - "Generative Models for Discovering Sparse Distributed Representations"
    - "Factorial Learning and the EM Algorithm"
    - "Extracting Support Data for a Given Task"

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: Probabilistic_Methods