Their Nature paper says "We trained the policy network p_sigma to classify positions according to expert moves played in the KGS data set. This data set contains 29.4 million positions from 160,000 games played by KGS 6 to 9 dan human players; 35.4% of the games are handicap games."
It is possible that they fed it some pro games after the Fan Hui games but before the Lee Sedol games, but that would be weird; at that point it was already learning from self-play rather than trying to match human moves.
That said, I don't think that Master's better performance comes from being trained on pro games. The AlphaGo version that played Lee Sedol played much more like a human pro than Master does.
There are multiple dan scales. The KGS scale is an amateur dan scale. I don't know how much the scales overlap generally, but I'd imagine a 9 pro-dan professional to be somewhere around 12 dan on amateur scale (pro scales also have more dense scaling). However, the scales reach the ceiling at 9 dan by convention.
Even the abbreviations differ: 9d (amateur dan) vs 9p (pro dan).
KGS 9 dan players are pros or amateurs that are professional level like former insei. The highest rank is almost 11d (it still says 9d, but the graph goes even higher):
