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 novel framework to synthesize images with soft-target age labels.

Keyphrases: age synthesis images

Dataset description:
CACD: The Cross-Age Celebrity Dataset (CACD) contains 163,446 images from 2,000 celebrities collected from the Internet. The images are collected from search engines using celebrity name and year (2004-2013) as keywords.
FFHQ-Aging: FFHQ-Aging is a Dataset of human faces designed for benchmarking age transformation algorithms as well as many other possible vision tasks. This dataset is an extention of the NVIDIA FFHQ dataset, on top of the 70,000 original FFHQ images, it also contains the following information for each image: * Gender information (male/female with confidence score) * Age group information (10 classes with confidence score) * Head pose (pitch, roll & yaw) * Glasses type (none, normal or dark) * Eye occlusion score (0-100, different score for each eye) * Full semantic map (19 classes, based on CelebAMask-HQ labels)
TITAN: TITAN consists of 700 labeled video-clips (with odometry) captured from a moving vehicle on highly interactive urban traffic scenes in Tokyo. The dataset includes 50 labels including vehicle states and actions, pedestrian age groups, and targeted pedestrian action attributes that are organized hierarchically corresponding to atomic, simple/complex-contextual, transportive, and communicative actions.
UTKFace: The UTKFace dataset is a large-scale face dataset with long age span (range from 0 to 116 years old). The dataset consists of over 20,000 face images with annotations of age, gender, and ethnicity.
D-HAZY: The D-HAZY dataset is generated from NYU depth indoor image collection. D-HAZY contains depth map for each indoor hazy image.
Adience: The Adience dataset, published in 2014, contains 26,580 photos across 2,284 subjects with a binary gender label and one label from eight different age groups, partitioned into five splits. The key principle of the data set is to capture the images as close to real world conditions as possible, including all variations in appearance, pose, lighting condition and image quality, to name a few.

Options:- A: CACD  B: FFHQ-Aging  C: TITAN  D: UTKFace  E: D-HAZY  F: Adience
["CACD", "UTKFace"]