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: "Shattering all sets of k points in "general position" requires (k 1)=2 parameters"
  Abstract: For classes of concepts defined by certain classes of analytic functions depending on n parameters, there are nonempty open sets of samples of length 2n + 2 which cannot be shattered. A slighly weaker result is also proved for piecewise-analytic functions. The special case of neural networks is discussed.

Connected through hyperedge HE-1 (induced by source paper "Networks of Spiking Neurons: The Third Generation of Neural Network Models"):
  with members:
    - "Vapnik-Chervonenkis Dimension of Recurrent Neural Networks"
    - "An Efficient Implementation of Sigmoidal Neural Nets in Temporal Coding with Noisy Spiking Neurons"
    - "A Delay-Line Based Motion Detection Chip"

Connected through hyperedge HE-2 (induced by source paper "Neural Networks with Quadratic VC Dimension"):
  with members:
    - "Vapnik-Chervonenkis Dimension of Recurrent Neural Networks"
    - "Dimension of Recurrent Neural Networks"
    - "Similar Classifiers and VC Error Bounds"

Connected through hyperedge HE-3 (induced by source paper "Critical Points for Least-Squares Problems Involving Certain Analytic Functions, with Applications to Sigmoidal Nets"):
  with members:
    - "BACKPROPAGATION CAN GIVE RISE TO SPURIOUS LOCAL MINIMA EVEN FOR NETWORKS WITHOUT HIDDEN LAYERS"

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