Is this the only example for which there is a misclassification? This can be the case because, at a fundamental level, ML models are probabilistic; even a 99% test accuracy model can fail over a trivial data point. I would suggest you test this over other examples (try something like 20-30 if manual); if it is working well on those examples, it is acceptable for a model to make this mistake. If the majority of the cases are misclassification, then I guess you have to try different architectures to design a better model.