Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
|   Col_0 | Col_1                   |   Col_2 | Col_3         |   Col_4 |   Col_5 | Col_6   |   Col_7 | Col_8   | Col_9   |   Col_10 |   Col_11 |   Col_12 |   Col_13 | Col_14   |   Col_15 | Col_16   |   Col_17 | Col_18   |
|--------:|:------------------------|--------:|:--------------|--------:|--------:|:--------|--------:|:--------|:--------|---------:|---------:|---------:|---------:|:---------|---------:|:---------|---------:|:---------|
|      73 | Eastern Equatoria       |      93 | Lafon         |    9302 |  133352 |         |  133352 | 0.0     |         |  54000   |   135745 |  45610.3 |    54000 |          |    54000 |          |  8356.89 |          |
|      48 | Lakes                   |      84 | Cueibet       |    8401 |  177652 | 1821.0  |  175831 | 976.0   | 516.0   |  71000   |   180839 |  61006.6 |    71000 |          |    69772 | 344.0    | 11223.5  | 552.0    |
|      43 | Northern Bahr el Ghazal |      82 | Aweil West    |    8204 |  302372 | 4087.0  |  298285 |         |         | 121000   |   307797 | 103420   |   121000 |          |   120866 |          | 44300.2  |          |
|      74 | Eastern Equatoria       |      93 | Kapoeta North |    9303 |  118052 |         |  118052 | 0.0     |         |  40377.1 |   120170 |  40377.1 |    18000 |          |    18000 |          |  9048.94 |          |
|      29 | Unity                   |      73 | Koch          |    7306 |  135205 | 1312.0  |  133893 | 30681.0 |         | 137630   |   137630 |  54343.5 |    82000 | 20000.0  |    81285 | 12645.0  | 17037.8  | 26585.0  |

Candidate column headers:
Total people in Need in WASH
Admin Level 1
Mid 2015 Population
Admin Level 2
Mid 2016 Population
Total people in Need
Mid 2015 Population4
Mid 2015 Population5
Pcode1
Total people in Need in HEALTH
Mid 2015 Population3
Total people in Need in FSL
Total people in Need in NUTRITION
Total people in Need in SHELTERS/NFI
ID  
Mid 2015 Population2
Total people in Need in CCCM
Total people in Need in EDUCATION
Pcode2



headers: Total people in Need in WASH
Admin Level 1
Mid 2015 Population
Admin Level 2
Mid 2016 Population
Total people in Need
Mid 2015 Population4
Mid 2015 Population5
Pcode1
Total people in Need in HEALTH
Mid 2015 Population3
Total people in Need in FSL
Total people in Need in NUTRITION
Total people in Need in SHELTERS/NFI
ID  
Mid 2015 Population2
Total people in Need in CCCM
Total people in Need in EDUCATION
Pcode2
label: ID  
Admin Level 1
Pcode1
Admin Level 2
Pcode2
Mid 2015 Population
Mid 2015 Population2
Mid 2015 Population3
Mid 2015 Population4
Mid 2015 Population5
Total people in Need
Mid 2016 Population
Total people in Need in HEALTH
Total people in Need in FSL
Total people in Need in CCCM
Total people in Need in WASH
Total people in Need in EDUCATION
Total people in Need in NUTRITION
Total people in Need in SHELTERS/NFI