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: "Relative Loss Bounds for Multidimensional Regression Problems"
  Abstract: We study on-line generalized linear regression with multidimensional outputs, i.e., neural networks with multiple output nodes but no hidden nodes. We allow at the final layer transfer functions such as the softmax function that need to consider the linear activations to all the output neurons. We also use a parameterization function which transforms parameter vectors maintained by the algorithm into the actual weights. The on-line algorithm we consider updates the parameters in an additive manner, analogous to the delta rule, but because the actual weights are obtained via the possibly nonlinear parameterization function they may behave in a very different manner. Our approach is based on applying the notion of a matching loss function in two different contexts. First, we measure the loss of the algorithm in terms of the loss that matches the transfer function used to produce the outputs. Second, the loss function that matches the parameterization function can be used both as a measure of distance between models in motivating the update rule of the algorithm and as a potential function in analyzing its relative performance compared to an arbitrary fixed model. As a result, we have a unified treatment that generalizes earlier results for the gradient descent and exponentiated gradient algorithms to multidimensional outputs, including multiclass logistic regression.

Connected through hyperedge HE-1 (induced by source paper "Exponentially many local minima for single neurons"):
  with members:
    - "BACKPROPAGATION CAN GIVE RISE TO SPURIOUS LOCAL MINIMA EVEN FOR NETWORKS WITHOUT HIDDEN LAYERS"
    - "BACKPROPAGATION SEPARATES WHERE PERCEPTRONS DO"
    - "On the Distribution of Performance from Multiple Neural Network Trials, On the Distribution of Performance"

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