The CiteSeerx project at the University of Arkansas uses a browsing interface is based on the Association for Computing Machinery's Computing Classification System (ACM CCS). CCS contains just 369 categories whereas the CiteSeerx database contains over 2 million documents. This results in more than 6500 documents per category, far too many to browse. To address this problem, we are exploring ways to automatically expand the CCS ontology. Previous work has focused on using clustering to automatically identify the new clas-ses. This work focuses on how to label the subclasses in a se-mantically meaningful way to that they can sup-port user browsing. We develop methods based on text mining from the subclass members to extract class la-bels. We evaluate three methods by comparing the suggested labels with human-assigned labels for existing categories.