Science has been experiencing vast gender imbalances in academic participation. Such inequalities have also been found in compensation, grant funding, hiring and promotions, authorship and citations. Due to the potential downstream effects of inequitable engagement with women-led and men-led work, the study of citation dynamics is a critical endeavor for understanding and addressing biases in science. Our study aims to identify the existence and potential causes of gender imbalances in citations. Our findings reveal a consistent pattern of homophilic citation behavior, where men-led teams show higher citational preference for men-led papers and lower preference for women-led papers, and vice versa for women-led teams. This behavior is observed across all subfields. Additionally, we found a positive correlation between the popularity of a subfield and the gendered citation imbalance, with more popular subfields (such as Machine Learning and Computer Vision) showing a higher imbalance.