Your task is to classify the citation intent within the following provided text from a computational linguistics research paper. The cited work is demarcated by "<cite>" and "</cite>". Determine the purpose of the cited work by selecting from the listed categories:

- Background: The cited paper underpins the subject matter.
- Motivation: The cited paper inspires or provides a rationale for the current research.
- Uses: The current work utilizes concepts or tools from the cited paper.
- Extends: The current work advances ideas or methods from the cited paper.
- Similarities: The current work identifies commonalities with the cited paper.
- Differences: The current work delineates its distinction from the cited paper.
- FutureWork: The cited paper is acknowledged as groundwork for prospective research.

Indicate the intents by listing them in a `json` array, e.g. ["Background", "Uses"]. More than one intent may be applicable. Do not include any extraneous text in your response.

Context with Citation:
Graph neural networks are a family of neural architectures (Scarselli et al., 2009; Gilmer et al., 2017) specifically devised to induce representation of nodes in a graph relying on its graph structure. Graph convolutional networks (GCNs) belong to this family. While GCNs were introduced BiRNN CNN Baseline<cite> (Bastings et al., 2017)</cite> 14.9 12.6 +Sem 15.6 13.4 +Syn<cite> (Bastings et al., 2017)</cite> 16.1 13.7 +Syn + Sem 15.8 14.3 for modeling undirected unlabeled graphs (Kipf and Welling, 2016) , in this paper we use a formulation of GCNs for labeled directed graphs, where the direction and the label of an edge are incorporated.
["Differences", "Background"]