You are provided with a research question, keyphrases about the question, a description of candidate datasets and dataset options. Read the description of popular datasets provided below and select the ones that can be used to validate the following research question. Use your knowledge of machine learning datasets to make the best judgement.
Your response should be formatted as a `json` array. For instance, for the query "Semi supervised image classification", a sample response might be: ["CIFAR-10", "CIFAR-100"]. Do not include any extra text in the response other than the answer array.

Query: A system for unsupervised domain adaptation of object detectors using pixel-level transformations and domain confusion

Keyphrases: object detection images

Dataset description:
GTA5: The GTA5 dataset contains 24966 synthetic images with pixel level semantic annotation. The images have been rendered using the open-world video game Grand Theft Auto 5 and are all from the car perspective in the streets of American-style virtual cities.
EMNIST: EMNIST (extended MNIST) has 4 times more data than MNIST. It is a set of handwritten digits with a 28 x 28 format.
Sim10k: SIM10k is a synthetic dataset containing 10,000 images, which is rendered from the video game Grand Theft Auto V (GTA5).
Libri-Adapt: Libri-Adapt aims to support unsupervised domain adaptation research on speech recognition models.
Office-Home: Office-Home is a benchmark dataset for domain adaptation which contains 4 domains where each domain consists of 65 categories. The four domains are: Art – artistic images in the form of sketches, paintings, ornamentation, etc.
Cityscapes: Cityscapes is a large-scale database which focuses on semantic understanding of urban street scenes. It provides semantic, instance-wise, and dense pixel annotations for 30 classes grouped into 8 categories (flat surfaces, humans, vehicles, constructions, objects, nature, sky, and void).

Options:- A: GTA5  B: EMNIST  C: Sim10k  D: Libri-Adapt  E: Office-Home  F: Cityscapes
["GTA5", "Sim10k", "Cityscapes"]