human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge groups papers that share a common citing paper.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "Critical Points for Least-Squares Problems Involving Certain Analytic Functions, with Applications to Sigmoidal Nets":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Shattering all sets of k points in "general position" requires (k 1)=2 parameters")
  Member "BACKPROPAGATION SEPARATES WHERE PERCEPTRONS DO":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Exponentially many local minima for single neurons")
  Member "Exponentially many local minima for single neurons":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "On the Distribution of Performance from Multiple Neural Network Trials, On the Distribution of Performance")

Question: Please tell me which class the citing paper (i.e., the source paper of the center hyperedge) 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