  Authors of research papers in the fields of mathematics, and other math-heavy
disciplines commonly employ the Mathematics Subject Classification (MSC) scheme
to search for relevant literature. The MSC is a hierarchical alphanumerical
classification scheme that allows librarians to specify one or multiple codes
for publications. Digital Libraries in Mathematics, as well as reviewing
services, such as zbMATH and Mathematical Reviews (MR) rely on these MSC labels
in their workflows to organize the abstracting and reviewing process.
Especially, the coarse-grained classification determines the subject editor who
is responsible for the actual reviewing process.
  In this paper, we investigate the feasibility of automatically assigning a
coarse-grained primary classification using the MSC scheme, by regarding the
problem as a multi-class classification machine learning task. We find that our
method achieves an (F_1)-score of over 77%, which is remarkably close to the
agreement of zbMATH and MR ((F_1)-score of 81%). Moreover, we find that the
method's confidence score allows for reducing the effort by 86% compared to the
manual coarse-grained classification effort while maintaining a precision of
81% for automatically classified articles.
