You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 talkingdata
【Schema】
# Table: label_categories
[
(label_id:INTEGER, Primary Key, Examples: [1, 2, 3]),
(category:TEXT, Examples: [game-game type, game-Game themes, game-Art Style])
]
# Table: gender_age
[
(device_id:INTEGER, Primary Key, Examples: [-9221086586254644858, -9221079146476055829, -9221066489596332354]),
(gender:TEXT, Examples: [M, F]),
(age:INTEGER, Examples: [29, 31, 38]),
(group:TEXT, Examples: [M29-31, M32-38, F29-32])
]
# Table: events_relevant
[
(event_id:INTEGER, Primary Key, Examples: [2, 6, 7]),
(device_id:INTEGER),
(timestamp:DATETIME),
(longitude:REAL, Examples: [1.0]),
(latitude:REAL, Examples: [0.0, 1.0])
]
# Table: gender_age_train
[
(device_id:INTEGER, Primary Key, Examples: [-9223067244542181226, -9222956879900151005, -9222754701995937853]),
(gender:TEXT, Examples: [M, F]),
(age:INTEGER, Examples: [24, 36, 29]),
(group:TEXT, Examples: [M23-26, M32-38, M29-31])
]
# Table: app_all
[
(app_id:INTEGER, Primary Key, Examples: [-9223281467940916832, -9222877069545393219, -9222785464897897681])
]
# Table: app_events_relevant
[
(event_id:INTEGER, Primary Key, Examples: [2, 6, 7]),
(app_id:INTEGER, Primary Key, Examples: [-8942695423876075857, -8022267440849930066, -5720078949152207372]),
(is_installed:INTEGER, Examples: [1]),
(is_active:INTEGER, Examples: [0, 1])
]
# Table: phone_brand_device_model2
[
(device_id:INTEGER, Primary Key, Examples: [-9223321966609553846, -9223067244542181226, -9223042152723782980]),
(phone_brand:TEXT, Primary Key, Examples: [小米, vivo, 三星]),
(device_model:TEXT, Primary Key, Examples: [红米note, Y19T, MI 3])
]
# Table: sample_submission
[
(device_id:INTEGER, Primary Key, Examples: [-9223321966609553846, -9223042152723782980, -9222896629442493034]),
(F23-:REAL, Examples: [0.0833]),
(F24-26:REAL, Examples: [0.0833]),
(F27-28:REAL, Examples: [0.0833]),
(F29-32:REAL, Examples: [0.0833]),
(F33-42:REAL, Examples: [0.0833]),
(F43+:REAL, Examples: [0.0833]),
(M22-:REAL, Examples: [0.0833]),
(M23-26:REAL, Examples: [0.0833]),
(M27-28:REAL, Examples: [0.0833]),
(M29-31:REAL, Examples: [0.0833]),
(M32-38:REAL, Examples: [0.0833]),
(M39+:REAL, Examples: [0.0833])
]
# Table: gender_age_test
[
(device_id:INTEGER, Primary Key, Examples: [-9223321966609553846, -9223042152723782980, -9222896629442493034])
]
# Table: app_events
[
(event_id:INTEGER, Primary Key, Examples: [2, 6, 7]),
(app_id:INTEGER, Primary Key, Examples: [-8942695423876075857, -8022267440849930066, -5720078949152207372]),
(is_installed:INTEGER, Examples: [1]),
(is_active:INTEGER, Examples: [0, 1])
]
# Table: app_labels
[
(app_id:INTEGER, Examples: [7324884708820027918, -4494216993218550286, 6058196446775239644]),
(label_id:INTEGER, Examples: [251, 406, 407])
]
# Table: events
[
(event_id:INTEGER, Primary Key, Examples: [1, 2, 3]),
(device_id:INTEGER, Examples: [29182687948017175, -6401643145415154744, -4833982096941402721]),
(timestamp:DATETIME, Examples: [2016-05-01 00:55:25]),
(longitude:REAL, Examples: [121.0, 104.0, 107.0]),
(latitude:REAL, Examples: [31.0, 30.0, 23.0])
]
【Foreign keys】
gender_age.device_id=phone_brand_device_model2.device_id
events_relevant.device_id=gender_age.device_id
app_events_relevant.app_id=app_all.app_id
app_events_relevant.event_id=events_relevant.event_id
app_events.event_id=events.event_id
app_labels.app_id=app_all.app_id
app_labels.label_id=label_categories.label_id

    【Question】
    List the included categories in the event ID of 155.

    【Evidence】
    

    Please read and understand the database schema carefully, and generate an executable SQL based on the user's question and evidence. 

SQL: SELECT DISTINCT T1.category FROM label_categories AS T1 INNER JOIN app_labels AS T2 ON T2.label_id = T1.label_id INNER JOIN app_events AS T3 ON T3.app_id = T2.app_id WHERE T3.event_id = 155