Gender identification of authors in literary texts is a compelling area of research within computational linguistics and natural language processing. Analyzing the gender of authors can uncover biases and socio-cultural dynamics of the past, deepening our understanding of historical texts. Inspired by the historical context where women often used male pseudonyms to navigate the literary world, this study seeks to determine an author's gender, relying on their written works using various classifiers, including language models. Our contributions include compiling a large-scale dataset of literary texts and conducting extensive experiments with different classification models. Our results show that the best-performing model, GPT2, achieved an impressive accuracy of 0.925.