Add note about k-D tree implementation (#334)

## Description

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Small note to memorialize our choice of scipy's k-D tree over sklearn's
k-D tree.

## TODO

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accomplish. -->

## Questions

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## Status

- [x] I have read the guidelines in
[CONTRIBUTING.md](https://github.com/icaros-usc/pyribs/blob/master/CONTRIBUTING.md)
- [x] I have formatted my code using `yapf`
- [x] I have tested my code by running `pytest`
- [x] I have linted my code with `pylint`
- [ ] ~I have added a one-line description of my change to the changelog
in `HISTORY.md`~
- [x] This PR is ready to go

ribs/archives/_cvt_archive.py

@@ -55,6 +55,9 @@ class CVTArchive(ArchiveBase):
         <../../tutorials/cma_mae.html>`_ for more info on thresholds, including
         the ``learning_rate`` and ``threshold_min`` parameters.
 
+    .. note:: For more information on our choice of k-D tree implementation, see
+        `#38 <https://github.com/icaros-usc/pyribs/issues/38>`_.
+
     Args:
         solution_dim (int): Dimension of the solution space.
         cells (int): The number of cells to use in the archive, equivalent to