classify text [gangguan, kebencian, porno, penyakit psikiatri atau mental, sifat perkauman, penghinaan agama, selamat untuk bekerja, mencederakan diri sendiri, seksis]

teks: An anecdote: I was once in a reading group with some ML practitioners from a FAANG-like company, discussing a modelling paper from an econometrics perspective. This approach heavily relies on understanding assumptions and analyzing the world in a logical manner (i.e. assuming that the world works in a certain way, creating one or two equations to summarize that worldview, and then fitting the data through MLE or some other process). The practitioners had difficulty following this approach and understanding why the economist was taking that approach from the paper!, return JSON {'label'}
{"label": "selamat untuk bekerja"}