Single-relay selection is a simple but efficient scheme for cooperative diversity among multiple user devices. However, the wrong selection of the best relay due to aged channel state information (CSI) remarkably degrades its performance, overwhelming this cooperative gain. Multi-relay selection is robust against channel aging but multiple timing offset (MTO) and multiple carrier frequency offset (MCFO) among spatially-distributed relays hinder its implementation in practical systems. In this paper, therefore, we propose a deep learning-based cooperative diversity method coined predictive relay selection (PRS) that chooses a single relay with the largest predicted CSI, which can alleviate the effect of channel aging while avoiding MTO and MCFO. Performance is evaluated analytically and numerically, revealing that PRS clearly outperforms the existing schemes with a negligible complexity burden.