Multinomial central limit theorem ($\sqrt{n}(\hat{p}-p)\Rightarrow N(0,\mathrm{diag}p-p{p}^T)$)

The vector of category counts is the sum of independent categorical indicator vectors. Their means are $p$ and covariance is $\mathrm{diag}p-p{p}^T$, so the multivariate central limit theorem gives the displayed limit. At uniform probabilities on $k$ categories, Pearson-standardized deviations have covariance equal to the orthogonal projection onto the $(k-1)$-dimensional subspace of zero-sum vectors. Their squared norm has an asymptotic chi-squared distribution with $k-1$ degrees of freedom.