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 Rectified Gaussian Distribution"
  Abstract: A simple but powerful modification of the standard Gaussian distribution is studied. The variables of the rectified Gaussian are constrained to be nonnegative, enabling the use of nonconvex energy functions. Two multimodal examples, the competitive and cooperative distributions, illustrate the representational power of the rectified Gaussian. Since the cooperative distribution can represent the translations of a pattern, it demonstrates the potential of the rectified Gaussian for modeling pattern manifolds.

Connected through hyperedge HE-1 (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"
    - "Data Mining for Association Rules with Unsupervised Neural Networks"
    - "Data Mining for Association Rules with Unsupervised Neural Networks"
    - "A HIERARCHICAL COMMUNITY OF EXPERTS"
    - "Continuous sigmoidal belief networks trained using slice sampling"
    - "Factor Analysis Using Delta-Rule Wake-Sleep Learning"

Connected through hyperedge HE-2 (induced by source paper "Representation of spatial orientation by the intrinsic dynamics of the head-direction cell ensemble: A theory"):
  with members:
    - "Separating hippocampal maps  Spatial Functions of the Hippocampal Formation and the"
    - "Learning Continuous Attractors in Recurrent Networks"
    - "Interpreting neuronal population activity by reconstruction: A unified framework with application to hippocampal place cells"

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