Wrong result of pandas.sparse.series.SparseSeries.loc with indexer of length 1
#### Code Sample, a copy-pastable example if possible

```python
In [3]: idx = pd.MultiIndex.from_tuples([('A', 0), ('A', 1), ('B', 0), ('C', 0), ('C', 1)])

In [4]: orig = pd.Series([1, np.nan, np.nan, 3, np.nan], index=idx)

In [5]: sparse = orig.to_sparse()

In [6]: sparse.loc[['A']]
Out[6]: 
A   NaN
dtype: float64
BlockIndex
Block locations: array([], dtype=int32)
Block lengths: array([], dtype=int32)

```
#### Problem description

It should return as below

#### Expected Output
``` python

In [7]: orig.loc[['A']].to_sparse()
Out[7]: 
A  0    1.0
   1    NaN
dtype: float64
BlockIndex
Block locations: array([0], dtype=int32)
Block lengths: array([1], dtype=int32)
```

#### Output of ``pd.show_versions()``

INSTALLED VERSIONS
------------------
commit: f65a6415f15d438432cc6954ead61b052c5d4d60
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.7.0-1-amd64
machine: x86_64
processor: 
byteorder: little
LC_ALL: None
LANG: it_IT.utf8
LOCALE: it_IT.UTF-8

pandas: 0.19.0+473.gf65a641
pytest: 3.0.6
pip: 8.1.2
setuptools: 28.0.0
Cython: 0.23.4
numpy: 1.12.0
scipy: 0.18.1
xarray: None
IPython: 5.1.0.dev
sphinx: 1.4.8
patsy: 0.3.0-dev
dateutil: 2.5.3
pytz: 2015.7
blosc: None
bottleneck: 1.2.0
tables: 3.2.2
numexpr: 2.6.0
feather: None
matplotlib: 2.0.0rc2
openpyxl: 2.3.0
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: 0.9.3
lxml: 3.6.4
bs4: 4.5.1
html5lib: 0.999
httplib2: 0.9.1
apiclient: 1.5.2
sqlalchemy: 1.0.15
pymysql: None
psycopg2: None
jinja2: 2.8
s3fs: None
pandas_datareader: 0.2.1

</details>
