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: "Critical Points for Least-Squares Problems Involving Certain Analytic Functions, with Applications to Sigmoidal Nets"
  Abstract: This paper deals with nonlinear least-squares problems involving the fitting to data of parameterized analytic functions. For generic regression data, a general result establishes the countability, and under stronger assumptions finiteness, of the set of functions giving rise to critical points of the quadratic loss function. In the special case of what are usually called "single-hidden layer neural networks," which are built upon the standard sigmoidal activation tanh(x) (or equivalently (1 + e x ) 1 ), a rough upper bound for this cardinality is provided as well.

Connected through hyperedge HE-1 (induced by source paper "Shattering all sets of k points in "general position" requires (k 1)=2 parameters"):
  with members:
    - "Neural Networks with Quadratic VC Dimension"
    - "Networks of Spiking Neurons: The Third Generation of Neural Network Models"

Connected through hyperedge HE-2 (induced by source paper "BACKPROPAGATION CAN GIVE RISE TO SPURIOUS LOCAL MINIMA EVEN FOR NETWORKS WITHOUT HIDDEN LAYERS"):
  with members:
    - "BACKPROPAGATION SEPARATES WHERE PERCEPTRONS DO"
    - "Exponentially many local minima for single neurons"

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