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:
Natural language generators for task-oriented dialog should be able to vary the style of the output while still effectively realizing the system dialog actions and their associated semantics. The use of neural natural language generation (NNLG) for training the response generation component of conversational agents promises to simplify the process of producing high quality responses in new domains by relying on the neural architecture to automatically learn how to map an input meaning representation to an output utterance. However, there has been little investigation of NNLGs for dialog that can vary their response style, and we know of no experiments on models that can generate responses that are different in style from those seen during training, while still maintaining semantic fidelity to the input meaning representation. Instead, work on stylistic transfer has focused on tasks where only coarse-grained semantic fidelity is needed, such as controlling the sentiment of the utterance (positive or negative), or the topic or entity under discussion [1, 2, 3] . Consider for example a training instance for the restaurant domain consisting of a meaning representation (MR) from the End-to-End (E2E) Generation Challenge 1 and a sample output from one of our neural generation models in Figure 1 [4, <cite>5]</cite> .
["Background"]