How to model a set of categorical values in the input of a neural network
Yes, the "one boolean per set element" is the standard way of encoding such a set.  This is known as a "one-hot encoding".  Yes, there will be a lot of input neurons, but that's not necessarily a serious problem; current procedures for training neural networks are able to handle millions of nodes with no problems.

If you know that $T$ will contain at most $m$ elements, in principle there are alternative encodings (e.g., use $m \lg n$ wires, where you use $\lg n$ wires for each element of $T$ to encode which element of $S$ it is)... but in practice I do not expect them to perform well.

In that case I think a better approach is to try to build a feature vector that is shorter than $n$ dimensions.  Do you have some domain knowledge you can use to identify attributes of the elements?  For instance, you could have a feature that says "how many elements of $T$ are red wines?" and "how many elements of $T$ are white wines?" and "how many elements of $T$ are dessert wines?" and so on.  You'll have to use your domain knowledge about the task to identify what attributes might be relevant to the classification task.  In this way, the number of input wires can be made much smaller than $n$.