Effectively organizing the vast and ever-growing body of research in scientific literature is crucial to advancing the field and supporting scholarly discovery. In this paper, we study the task of fine-grained hierarchical multi-label classification of scholarly articles, using a structured taxonomy. Specifically, we investigate whether incorporating hierarchical information in a classification method can improve performance compared to conventional flat classification approaches. To this end, we suggest and evaluate different strategies for the classification, on three different axes: selection of positive and negative samples; soft-to-hard label mapping; hierarchical post-processing policies that utilize taxonomy-related requirements to update the final labeling. Experiments demonstrate that flat baselines constitute powerful baselines, but the infusion of hierarchical knowledge leads to better recall-focused performance based on use-case requirements.