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 "Pattern analysis and synthesis in attractor neural networks"):
  Abstract: The representation of hidden variable models by attractor neural networks is studied. Memories are stored in a dynamical attractor that is a continuous manifold of fixed points, as illustrated by linear and nonlinear networks with hidden neurons. Pattern analysis and synthesis are forms of pattern completion by recall of a stored memory. Analysis and synthesis in the linear network are performed by bottom-up and top-down connections. In the nonlinear network, the analysis computation additionally requires rectification nonlinearity and inner product inhibition between hidden neurons. One popular approach to sensory processing is based on generative models, which assume that sensory input patterns are synthesized from some underlying hidden variables. For example, the sounds of speech can be synthesized from a sequence of phonemes, and images of a face can be synthesized from pose and lighting variables. Hidden variables are useful because they constitute a simpler representation of the variables that are visible in the sensory input. Using a generative model for sensory processing requires a method of pattern analysis. Given a sensory input pattern, analysis is the recovery of the hidden variables from which it was synthesized. In other words, analysis and synthesis are inverses of each other. There are a number of approaches to pattern analysis. In analysis-by-synthesis, the synthetic model is embedded inside a negative feedback loop[1]. Another approach is to construct a separate analysis model[2]. This paper explores a third approach, in which visible-hidden pairs are embedded as attractive fixed points, or attractors, in the state space of a recurrent neural network. The attractors can be regarded as memories stored in the network, and analysis and synthesis as forms of pattern completion by recall of a memory. The approach is illustrated with linear and nonlinear network architectures. In both networks, the synthetic model is linear, as in principal

Includes members:
  Member "Learning Generative Models with the Up-Propagation Algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Unsupervised Learning by Convex and Conic Coding")
  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 "Generative Models for Discovering Sparse Distributed Representations":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Continuous sigmoidal belief networks trained using slice sampling")
  Member "Learning Continuous Attractors in Recurrent Networks":
    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")

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