Based on the given context, craft a common-sense question, especially those that are LONG, INTERESTING, and COMPLEX. The goal is to write questions that are easy for humans and hard for AI machines! To create such questions, here are some suggestions: A. What may (or may not) be the plausible reason for an event? B. What may (or may not) happen before (or after, or during) an event? C. What may (or may not) be a plausible fact about someone (or something)? D. What may (or may not) happen if an event happens (or did not happen)? You can also create other types of questions. DO NOT make your question answerable without looking at the context, or question of which the correct answer can be directly extracted from the context. DO NOT ask a question that requires very specialized knowledge that is not common sense. DO NOT ask too simple or too short questions. Your question must be related to the context and answerable with common sense. Try to add more variations and complexity to the questions.

Example Input: Context: Board : State : MaineAuthor : wassamatta uWe had so much fun stealing wassamatta u 's boxers , then parading around in them in costume!Yes , and I ESPECIALLY liked the booby - trap left for me in my bunk ! I was trying SO hard not to wake the people in my cabin who were asleep , when I leaned over into my bunk and got tangled in a web of sticky tape and Mardi Gras beads ! How those people slept through my laughing , I will never know ! Thanks , Panty Party !.
Example Output: Why did the person enjoy being booby - trapped in their own bed ?

Example Input: Context: jolly good ! my new furniture got delivered this morning . the front living room has become a temporary storeroom for my microwave , fridge etc . it 'll probably stay that way until i find a place to rent .
Example Output: What is the reason they had new furniture delivered ?

Example Input: Context: They then asked if we had seen anything . We told them ; " Unfortunately no -- we were facing forward in the store . " Then they asked if we had any cameras that looked out onto the parking lot .
Example Output:
Who was interviewing them ?