Since you are going for many to one sequence modeling, you don't need to pad zeros to your output. The most easygoing thing would be to perform classification at last time-step i.e after RNN/LSTM sees the 5th input.
Answering to your question, the dimension of your 3D input tensor will be:
[batch_size, sequence_length, input_dimensionality],
where sequence_length is 5 in your state (row 1-5, 7-11, 13-17, etc.), and input_dimensionality is also 5 (i.e. column A- E). Batch_size depends on the number of samples (also how much reliable is your data), if you have more than 10,000 examples then batch size of 30-50 should be okay.
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