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: "Back Propagation is Sensitive to Initial Conditions"
  Abstract: This paper explores the effect of initial weight selection on feed-forward networks learning simple functions with the back-propagation technique. We first demonstrate, through the use of Monte Carlo techniques, that the magnitude of the initial condition vector (in weight space) is a very significant parameter in convergence time variability. In order to further understand this result, additional deterministic experiments were performed. The results of these experiments demonstrate the extreme sensitivity of back propagation to initial weight configuration.

Connected through hyperedge HE-1 (induced by source paper "Neural Network Implementation in SAS R Software"):
  with members:
    - "Neural Networks and Statistical Models Proceedings of the Nineteenth Annual SAS Users Group International Conference,"

Connected through hyperedge HE-2 (induced by source paper "Scaling-up RAAMs"):
  with members:
    (none)

Connected through hyperedge HE-3 (induced by source paper "Hyperplane Dynamics as a Means to Understanding Back-Propagation Learning and Network Plasticity"):
  with members:
    - "Submitted to Circuits, Systems and Signal Processing Neural Network Constructive Algorithms: Trading Generalization for Learning Efficiency?"

Connected through hyperedge HE-4 (induced by source paper "Multiassociative Memory"):
  with members:
    - "Cholinergic suppression of transmission may allow combined associative memory function and self-organization in the neocortex."

Connected through hyperedge HE-5 (induced by source paper "Statistical Tests for Comparing Supervised Classification Learning Algorithms"):
  with members:
    - "Pessimistic decision tree pruning based on tree size"
    - "Rigorous Learning Curve Bounds from Statistical Mechanics"
    - "A Comparative Study of ID3 and Backpropagation for English Text-to-Speech Mapping"

Connected through hyperedge HE-6 (induced by source paper "Representing and Learning Visual Schemas in Neural Networks for Scene Analysis"):
  with members:
    - "Book Review  Introduction to the Theory of Neural Computation Reviewed by: 2"
    - "Priming, Perceptual Reversal, and Circular Reaction in a Neural Network Model of Schema-Based Vision"
    - "VISOR: Schema-based Scene Analysis with Structured Neural Networks"

Connected through hyperedge HE-7 (induced by source paper "Experiments on the Transfer of Knowledge between Neural Networks Reprinted from: Computational Learning Theory and"):
  with members:
    - "A Comparative Study of ID3 and Backpropagation for English Text-to-Speech Mapping"

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