You will be shown an excerpt from a computer science scientific research paper, followed by a question about the paper. Please answer the question. Do not include any text in your response other than the answer.

Context: We attack the problem of few-shot learning by addressing the key issue of overfitting. Since data is severely limited, we work under the assumption that a classifier should have a very simple inductive bias. Our approach, prototypical networks, is based on the idea that there exists an embedding in which points cluster around a single prototype representation for each class. In order to do this, we learn a non-linear mapping of the input into an embedding space using a neural network and take a class’s prototype to be the mean of its support set in the embedding space. Classification is then performed for an embedded query point by simply finding the nearest class prototype. We follow the same approach to tackle zero-shot learning; here each class comes with meta-data giving a high-level description of the class rather than a small number of labeled examples. We therefore learn an embedding of the meta-data into a shared space to serve as the prototype for each class. Classification is performed, as in the few-shot scenario, by finding the nearest class prototype for an embedded query point. [Prototypical networks differ from matching networks in the few-shot case with equivalence in the one-shot scenario. Matching networks [29] produce a weighted nearest neighbor classifier given the support set, while prototypical networks produce a linear classifier when squared Euclidean distance is used. In the case of one-shot learning, ck = xk since there is only one support point per class, and matching networks and prototypical networks become equivalent.]

Question: Is the paper's method also could be interpreted as a weighted nearest-neighbor classifier applied within an embedding space?
[The paper’s approach, prototypical networks, is based on the idea that there exists an embedding in which points cluster around a single prototype representation for each class. Prototypical networks differ from matching networks in the few-shot case with equivalence in the one-shot scenario. Matching networks produce a weighted nearest neighbor classifier given the support set, while prototypical networks produce a linear classifier when squared Euclidean distance is used. In the case of one-shot learning, ck = xk since there is only one support point per class, and matching networks and prototypical networks become equivalent. When they become equivalent, then the paper’s approach and matching networks (which produce a weighted nearest neighbor classifier), can be interpreted the same.]