Which feature selection I should trust?
It is generally not best practice to trust ML techniques to do the work for you. A data scientist knowns on what principles the methods that he or she applies work, and then stills tries multiple in the same setting, to see which methods best apply to the domain at hand. How well certain measures work can be a clue to the structure of your data. 

Right off the bat it is important to understand that Weka offers 3 kinds of feature selection methods: Filters, a Wrapper and PCA. Filters apply criteria (mostly based on a measure of how it divides a set) for feature suitedness (variations on how well they divide the search space) , wrappers use a learning algorithm for their evaluation and PCA doesn't so much select features as that it recombines features to maximize variance in the dimensions.

That being said: I suggest you start looking at InformationGainEval a method that uses information gain to select attributes. It is quite popular and easy to understand. You can find a good explantation here: 
https://stackoverflow.com/questions/33982943/how-the-selection-happens-in-infogainattributeeval-in-weka-feature-selection. Than work your way through the others (the documentation is accessible from the GUI).