Individuals with Autism Spectrum Disorder (ASD) are known to have significantly limited social interaction abilities, which are often manifested in different non-verbal cues of communication such as facial expression, atypical eye gaze response. While prior works leveraged the role of pupil response for screening ASD, limited works have been carried out to find the influence of emotion stimuli on pupil response for ASD screening. We, in this paper, design, develop, and evaluate a light-weight LSTM (Long-short Term Memory) model that captures pupil responses (pupil diameter, fixation duration, and fixation location) based on the social interaction with a virtual agent and detects ASD sessions based on short interactions. Our findings demonstrate that all the pupil responses vary significantly in the ASD sessions in response to the different emotion (angry, happy, neutral) stimuli applied. These findings reinforce the ASD screening with an average accuracy of 77%, while the accuracy improves further (>80%) with respect to angry and happy emotion stimuli.