You are interested in fitting a neural network for a regression problem by minimizing the sum of squared errors (SSE). You will use backpropagation to calculate the derivatives as part of the optimization algorithm. The squared residual of the n-th data point is Rn, the neural network response for the n-th data point is fn. The residual or error of the n-th data point is n. The k-th hidden unit's non-linear response for the n-th data point is hn,k. The output layer weight for the k-th hidden unit is k, the hidden unit parameter weight for the d-th input within the k-th hidden unit is d,k. The d-th input's value for the n-th data point is xn,d. Lastly, the hidden unit error for n-th data point associated with the k-th hidden unit is n,k. Which of the expressions listed below corresponds to the derivative of the squared residual, Rn, with respect to the output layer weight, k ? kRnkRnkRnkRnkRn=2nhn,k=2n,kxn,d=2n,khn,k=2nn,k=2hn,kxn,d