LSTM hidden state?

On the following very simple example, why the hidden state consists of 2 Tensors? From what I understand, isn’t it supposed to be just a Tensor of size 20?

import torch
import torch.autograd as autograd
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.autograd import Variable

rnn = nn.LSTM(input_size=10, hidden_size=20)

input = Variable(torch.randn(50, 1, 10))#seq_len x batch x input_size

output, hn = rnn(input)

print (hn)

lstm has both h and c hidden states

Can you please explain the difference between the two and what each is suited for? Should I just concatenate them?