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: "The wake-sleep algorithm for unsupervised neural networks"
  Abstract: We describe an unsupervised learning algorithm for a multilayer network of stochastic neurons. Bottom-up recognition connections convert the input into representations in successive hidden layers and top-down generative connections reconstruct the representation in one layer from the representation in the layer above. In the wake phase, neurons are driven by recognition connections, and generative connections are adapted to increase the probability that they would reconstruct the correct activity vector in the layer below. In the sleep phase, neurons are driven by generative connections and recognition connections are adapted to increase the probability that they would produce Supervised learning algorithms for multilayer neural networks face two problems: They require a teacher to specify the desired output of the network and they require some method of communicating error information to all of the connections. The wake-sleep algorithm finesses both these problems. When there is no teaching signal to be matched, some other goal is required to force the hidden units to extract underlying structure. In the wake-sleep algorithm the goal is to learn representations that are economical to describe but allow the input to be reconstructed accurately. Each input vector could be communicated to a receiver by first sending its hidden representation and then sending the difference between the input vector and its top-down reconstruction from the hidden representation. The aim of learning is to minimize the description length which is the total number of bits that would be required to communicate the input vectors in this way [1]. No communication actually takes place, but minimizing the description length that would be required forces the network to learn economical representations that capture the underlying regularities in the data [2]. the correct activity vector in the layer above.

Connected through hyperedge HE-1 (induced by source paper "Cortical Mechanisms of Visual Recognition and Learning: A Hierarchical Kalman Filter Model"):
  with members:
    - "Cholinergic suppression of transmission may allow combined associative memory function and self-organization in the neocortex."
    - "Hierarchical Mixtures of Experts and the EM Algorithm"
    - "Regularization Theory and Neural Networks Architectures"

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