A new method of Gaussian process dynamic model (GPDM), named class-aware shared GPDM (CSGPDM), is presented in this paper. One of the most difference between our CSGPDM and existing GPDM is considering class information which helps to build the class label-based latent space being effective for the following class-related tasks. In terms of representation learning, CSGPDM is optimized by considering not only a non-linear relationship but also time-series relation and discriminative information of each class label. Then CSGPDM can reflect the following three points to the estimated latent space: i) the relationship between heterogeneous input sets, ii) time-series relations lurked in each input data, and iii) class information. Therefore, when input heterogeneous sets of features have time-series relations and class information, the above CSGPDM-based latent space can be beneficial for the obtaining the new CSGPDM-based feature sets for the post classification and estimating one side of the lacking samples by bridging the input heterogeneous feature sets via the latent space. Experimental results show that the estimated CSGPDM-based latent space outperformed those of GPDM and shared GPDM (SGPDM).