Culture is a collective social norm of human societies that often influences a person's values, thoughts, and social behaviors during interactions at an individual level. In this work, we present a computational analysis toward automatic assessing an individual's culture attribute of power distance, i.e., a measure of his/her belief about status, authority and power in organizations, by modeling their expressive prosodic structures during social encounters with people of different power status. Specifically, we propose a center-loss embedded network architecture to jointly consider the effect of social interaction contexts on individuals' prosodic manifestations in order to learn an enhanced representation for power distance recognition. Our proposed prosodic network achieves an overall accuracy of 78.6% in binary classification task of recognizing high versus low power distance. Our experiment demonstrates an improved discriminability (17.6% absolute improvement) over prosodic neural network without social context enhancement. Further visualization reveals that the diversity in the prosodic manifestation for individuals with low power distance seems to be higher than those of high power distance.