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: In convolutional neural networks, a local region of a pixel consists of pixels with array indices within certain Manhattan distance (kernel size) of the pixel. In a point set sampled from a metric space, the neighborhood of a point is defined by metric distance. A few very recent works [20, 28] have studied how to apply deep learning to unordered sets. They ignore the underlying distance metric even if the point set does possess one. As a result, they are unable to capture local context of points and are sensitive to global set translation and normalization. In this work, we target at points sampled from a metric space and tackle these issues by explicitly considering the underlying distance metric in our design. Suppose that \mathcal{X}=(M,d) is a discrete metric space whose metric is inherited from a Euclidean space \mathbb{R}^{n}, where M\subseteq\mathbb{R}^{n} is the set of points and d is the distance metric. In addition, the density of M in the ambient Euclidean space may not be uniform everywhere. We are interested in learning set functions f that take such \mathcal{X} as the input (along with additional features for each point) and produce information of semantic interest regrading \mathcal{X}. In practice, such f can be classification function that assigns a label to \mathcal{X} or a segmentation function that assigns a per point label to each member of M.

Question: What distance metric this paper uses
[In the paper, in a point set sampled from a metric space, the neighborhood of a point is defined by metric distance. For instance, suppose \mathcal{X}=(M,d) is a discrete metric space whose metric is inherited from a Euclidean space \mathbb{R}^{n}, where M\subseteq\mathbb{R}^{n} is the set of points and d is the distance metric. The paper targets at points sampled from a metric space and explicitly considers the underlying distance metric in its design.]