human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge groups papers that share a common citing paper.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "References "Using Neural Networks to Identify Jets", Kohonen, "Self Organized Formation of Topologically Correct Feature":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Soft Computing: the Convergence of Emerging Reasoning Technologies")
  Member "Unsupervised Learning by Convex and Conic Coding":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Factorial Learning and the EM Algorithm")
  Member "Pattern analysis and synthesis in attractor neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Learning Generative Models with the Up-Propagation Algorithm")
  Member "The Rectified Gaussian Distribution":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "Representation of spatial orientation by the intrinsic dynamics of the head-direction cell ensemble: A theory")
  Member "Data Mining for Association Rules with Unsupervised Neural Networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "The EM Algorithm for Mixtures of Factor Analyzers")
  Member "Data Mining for Association Rules with Unsupervised Neural Networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "The EM Algorithm for Mixtures of Factor Analyzers")
  Member "A HIERARCHICAL COMMUNITY OF EXPERTS":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "The EM Algorithm for Mixtures of Factor Analyzers")
  Member "Continuous sigmoidal belief networks trained using slice sampling":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "Markov Chain Monte Carlo Methods Based on `Slicing' the Density Function")

Question: Please tell me which class the citing paper (i.e., the source paper of the center hyperedge) 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