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: "BACKPROPAGATION CAN GIVE RISE TO SPURIOUS LOCAL MINIMA EVEN FOR NETWORKS WITHOUT HIDDEN LAYERS"
  Abstract: We give an example of a neural net without hidden layers and with a sigmoid transfer function, together with a training set of binary vectors, for which the sum of the squared errors, regarded as a function of the weights, has a local minimum which is not a global minimum. The example consists of a set of 125 training instances, with four weights and a threshold to be learnt. We do not know if substantially smaller binary examples exist.

Connected through hyperedge HE-1 (induced by source paper "Exponentially many local minima for single neurons"):
  with members:
    - "BACKPROPAGATION SEPARATES WHERE PERCEPTRONS DO"
    - "On the Distribution of Performance from Multiple Neural Network Trials, On the Distribution of Performance"
    - "Relative Loss Bounds for Multidimensional Regression Problems"

Connected through hyperedge HE-2 (induced by source paper "BACKPROPAGATION SEPARATES WHERE PERCEPTRONS DO"):
  with members:
    - "LINEAR SYSTEMS WITH SIGN-OBSERVATIONS"
    - "Exponentially many local minima for single neurons"

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:
    - "Shattering all sets of k points in "general position" requires (k 1)=2 parameters"

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