"Cheap iterations" led me to expect a 1st order method, not requiring computing the matrix of 2nd derivatives, which is the bottleneck in large systems. But this does indeed rely on the Hessian, so I'm not sure what makes the iterations "cheap".
Update: your other comment clarified this for me. It's cheap relative to other methods that work with non-self-concordant objectives.