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:
This tutorial addresses the fundamentals of statistical models and neural networks, and focus on a series of advanced Bayesian models and deep models including hierarchical Dirichlet process , Chinese restaurant process (Blei et al., 2010) , hierarchical Pitman-Yor process (Teh, 2006) , Indian buffet process (Ghahramani and Griffiths, 2005) , recurrent neural network (Mikolov et al., 2010; Van Den Oord et al., 2016) , long short-term memory (Hochreiter and Schmidhuber, 1997; , sequence-to-sequence model (Sutskever et al., 2014), variational auto-encoder (Kingma and Welling, 2014) , generative adversarial network (Goodfellow et al., 2014) , attention mechanism (Chorowski et al., 2015; <cite>Seo et al., 2016)</cite> , memory-augmented neural network (Graves et al., 2014; Graves et al., 2014) , stochastic neural network Miao et al., 2016) , predictive state neural network (Downey et al., 2017) , policy gradient (Yu et al., 2017) and reinforcement learning (Mnih et al., 2015) .
["Background"]