I have heard people using the term background class in the following to scenarios:
For a class which has a very high number of instances compared to other classes in a classification problem
Sometimes just creating a class like "others" and adding all those instances for which enough features are not available to discriminatively decide the class which they belong to. For example, in Section 2 of this paper, https://www.robots.ox.ac.uk/~vgg/publications/2015/Cimpoi15/cimpoi15.pdf (this paper deals with building features for performing texture/material recognition in images). I have quoted the particular text which talks about a background class below:
In particular, we build on the Open Surfaces (OS) dataset that was recently introduced by Bell et al.  in computer graphics. OS comprises 25,357 images, each containing a number of high-quality texture/material segments. Many of these segments are annotated with additional attributes such as the material name, the viewpoint, the BRDF, and the object class. Not all segments have a complete set of annotations; the experiments in this paper focus on the 58,928 that contain material names. Since material classes are highly unbalanced, only the materials that contain at least 400 examples are considered. This result in 53,915 annotated material segments in 10,422 images spanning 23 different classes. Images are split evenly into training, validation, and test subsets with 3,474 images each. Segment sizes are highly variable, with half of them being relatively small, with an area smaller than 64 × 64 pixels. While the lack of exhaustive annotations makes it impossible to define a complete background class, several less common materials (including for example segments that annotators could not assign to a material) are merged in an “other” class that acts as pseudo-background.
Are there other scenarios where this term is used? Also, why exactly is it called a background class?