Please bear with me as I am new to NLP. I am specifically using tensorflow's universal sentence encoder: https://tfhub.dev/google/universal-sentence-encoder-large/3
I am clustering text based on the cosine similarity of the embedding produced by the model and I want to see what cluster a new text would most likely lie in. I was going to compare the new text embedding to the mean/median of all the embeddings within a cluster to see which cluster it would most likely lie in. Would taking the mean/median of the cluster's vectors "represent" the general idea of the cluster or will the vector not represent what I am looking for?