In general, there's no reason to assume that the distributions over words—topics, in model parlance—will give highest probability to the most natural "label" for the topic.
You can see this in the sample topics and corresponding top words shown in the paper introducing LDA:
"Arts" "Budgets" "Chidren" "Education"
NEW MILLION CHILDREN SCHOOL
FILM TAX WOMEN STUDENTS
SHOW PROGRAM PEOPLE SCHOOLS
MUSIC BUDGET CHILD EDUCATION
MOVIE BILLION YEARS TEACHERS
PLAY FEDERAL FAMILIES HIGH
MUSICAL YEAR WORK PUBLIC
BEST SPENDING PARENTS TEACHER
ACTOR NEW SAYS BENNETT
FIRST STATE FAMILY MANIGAT
YORK PLAN WELFARE NAMPHY
OPERA MONEY MEN STATE
THEATER PROGRAMS PERCENT PRESIDENT
ACTRESS GOVERNMENT CARE ELEMENTARY
LOVE CONGRESS LIFE HAITI
This, I'd argue, shows that the choice of top word is a little problematic.
"Children" is both top term and topic, but "arts"—a pretty natural label for the first topic—doesn't even appear in its top words. "Education" is in the top five, "budgets" too if we're flexible. Choosing the top word of each makes no sense in the first two cases, and reasonable sense in the latter.
Of course, these labels were manually, subjectively chosen by the authors, and you might have labelled them differently. I myself would have used "family" instead of "children" for the third. More, the topics would change if you altered $k, \alpha$ or $\eta$.
You can set $k$ to minimize perplexity, but will these be meaningful to typical readers? At what threshold should each mixing proportion reach before labeling a document with that topic?
One extension, supervised LDA, allows you to both fit distributions over words and model responses. Meaning, if you have or can develop a labelled corpus, you could build models that predict whether a given label applied to a new document. This would let you dodge the problem of finding meaning in fitted topics: You would start with what's meaningful and fit topics to it.