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:
Method 1: Maximum Attention Weights (MAX) Given a token A in a sentence, a selfattention mechanism is designed to assign high attention weights on tokens that have some kind of relationship with token A (Vaswani et al., 2017) . Therefore, for a given token A, a token B that has the highest attention weight with respect to the token A should be related to token A. Our aim is to investigate whether this relation maps to a universal dependency relation. We assign a relation (w i , w j ) between word w i and w j if j = argmax W [i] for each row (that corresponds to a word in attention matrix) i in attention matrix W . Based on this simple strategy, we extract relations for all sentences in our evaluation datasets. This method is similar to<cite> Clark et al. (2019)</cite> , and attempts to recover individual arcs between words; the relations extracted using this method need not form a valid tree, or even be fully connected, and the resulting edge directions may or may not match the canonical directions. Hence, we evaluate the resulting arcs individually and ignore their direction. After extracting dependency relations from all heads at all layers, we take the maximum UUAS over all relations types.
["Extends", "Similarities"]