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: "Exponentially many local minima for single neurons"
  Abstract: We show that for a single neuron with the logistic function as the transfer function the number of local minima of the error function based on the square loss can grow exponentially in the dimension.

Connected through hyperedge HE-1 (induced by source paper "On the Distribution of Performance from Multiple Neural Network Trials, On the Distribution of Performance"):
  with members:
    - "What Size Neural Network Gives Optimal Generalization? Convergence Properties of Backpropagation"
    - "Lessons in Neural Network Training: Overfitting Lessons in Neural Network Training: Overfitting May be Harder"
    - "A Unifying View of Some Training Algorithms for Multilayer Perceptrons with FIR Filter Synapses"
    - "Statistical Evaluation of Neural Network Experiments: Minimum Requirements and Current Practice"

Connected through hyperedge HE-2 (induced by source paper "BACKPROPAGATION SEPARATES WHERE PERCEPTRONS DO"):
  with members:
    - "LINEAR SYSTEMS WITH SIGN-OBSERVATIONS"
    - "BACKPROPAGATION CAN GIVE RISE TO SPURIOUS LOCAL MINIMA EVEN FOR NETWORKS WITHOUT HIDDEN LAYERS"

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

Connected through hyperedge HE-4 (induced by source paper "Relative Loss Bounds for Multidimensional Regression Problems"):
  with members:
    (none)

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: Theory