In the original paper makes it clear that the nearest neighbours are from the minority class, so it appears that Liu et al. are mistaken.

BTW if you are using a modern classifier, such as an SVM, that can implement cost-sensitive learning and has a good means of avoiding overfitting, then I would advise against using SMOTE. Techniques such as regularisation have a lot of solid theory behind them, the method used by SMOTE has none. I would also advise using a probabilistic classifier for problems with unequal misclassification costs etc. as you can account for the misclassification costs after the model has been fitted to the data.

In the original paper makes it clear that the nearest neighbours are from the minority class, so it appears that Liu et al. are mistaken.

Having said which, I am not sure the paper is entirely consistent with most implementations (or indeed itself)

I think for the synthetic point to be on a line joining the selected minority point with it's nearest neighbour, $gap$ would have to be assigned outside the loop over attributes, not inside it.

BTW if you are using a modern classifier, such as an SVM, that can implement cost-sensitive learning and has a good means of avoiding overfitting, then I would advise against using SMOTE. Techniques such as regularisation have a lot of solid theory behind them, the method used by SMOTE has none. I would also advise using a probabilistic classifier for problems with unequal misclassification costs etc. as you can account for the misclassification costs after the model has been fitted to the data.