Let $w \sim N(0, \sigma^2 I_d)$ and $w \in \mathbb{R}^d$. Denote $w_i$ as one value from the vector $w$. How can we prove that $\Bbb E[\|w\|^{j-1} \cdot w_i\cdot w_k] = 0$ for $i \neq k$ and $j \ge 2, j\in \mathbb{N}$?
Can it be said that $\ell^2$ norm to the power of $j-1$ (that is, $\|w\|^{j-1}$) is independent from the individual values of vector $w$? If so, then automatically $$\Bbb E[\|w\|^{j-1} \cdot w_i\cdot w_k] = \Bbb E[\|w\|^{j-1}] \cdot \Bbb E[w_i\cdot w_k] = 0.$$
I just need a clue whether this can be done or not, and provide me with the possible idea how I should tackle this? This is the university textbook course question and it has been part of the much complex question that I cannot disclose due to it being too long, but the part I just wrote seemed to me as the smallest understandable part of the question. By the way, I have a Computer Science background, and the course I am attending is related to High Dimensional Statistics book by Wainwright.