Why performs the NN better with OneHotEncoding?
Short answer - depending on the use of the feature in the classification and according to the implementation of the classifier you use, you decide if to use OHE or not. If the feature is a category, such that the rank has no meaning (for example, the suit of the card 1=clubs, 2=hearts...) then you should use OHE (for frameworks that require categorical distinction), because ranking it has no meaning. If the feature has a ranking meaning, with regards to the classification, then keep it as-is (for example, the probability of getting a certain winnig hand).
As you did not specify to what task you are using the NN nor the loss function and a lot of other things - I can only assume that when you say "...my nn performs better with OHE" you want to classify a combination to a class of poker hands and in this scenario the data just presents for the learner the classes to distinguish between them (as a category not as a rank). You can add a feature of the probability and/or strength of the hand etc. which will be a ranking feature - as for the resulted classifier, that's a whole other topic if adding it will improve or not (meaning the number of features to classification performance).
Hope I understood you correctly.
Note - this is a big question and there is a lot of hand waving, but this is the scope.
i have a question just for a general case. So i am working with the poker-hand-dataset, which has 10 possible outputs from 0-9, each number gives a poker-hand, for example royal flush.
So i read in the internet, that it is necessary to use OHE in a multiclass problem because if not there would be like a artificial order, for example if you work with cities. But in my case with the poker hands there is a order from one pair over flush and straight to royal flush, right?
Even though my nn performs better with OHE, but it works also (but bad) without.
So why does it work better with the OHE? I did a Dense Network with 2 hidden layer.