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

    【Schema】
    【DB_ID】 cookbook
【Schema】
# Table: Quantity
[
(quantity_id:INTEGER, Primary Key, Examples: [1, 2, 3]),
(recipe_id:INTEGER, Examples: [214, 215, 216]),
(ingredient_id:INTEGER, Examples: [1613, 3334, 2222]),
(max_qty:REAL, Examples: [2.0, 0.25, 0.5]),
(min_qty:REAL, Examples: [2.0, 0.25, 0.5]),
(unit:TEXT, Examples: [cup(s), teaspoon(s), 14 oz. can(s)]),
(preparation:TEXT, Examples: [melted, or water, divided]),
(optional:TEXT, Examples: [FALSE, TRUE])
]
# Table: Nutrition
[
(recipe_id:INTEGER, Primary Key, Examples: [214, 215, 216]),
(protein:REAL, Examples: [5.47, 5.7, 4.9]),
(carbo:REAL, Examples: [41.29, 23.75, 26.88]),
(alcohol:REAL, Examples: [0.0, 1.93, 2.7]),
(total_fat:REAL, Examples: [11.53, 1.08, 1.1]),
(sat_fat:REAL, Examples: [2.21, 0.58, 0.06]),
(cholestrl:REAL, Examples: [1.39, 3.48, 3.46]),
(sodium:REAL, Examples: [260.78, 46.17, 41.79]),
(iron:REAL, Examples: [0.81, 0.57, 0.37]),
(vitamin_c:REAL, Examples: [8.89, 13.02, 6.13]),
(vitamin_a:REAL, Examples: [586.2, 2738.24, 1521.1]),
(fiber:REAL, Examples: [0.87, 0.62, 0.34]),
(pcnt_cal_carb:REAL, Examples: [56.8, 67.38, 78.45]),
(pcnt_cal_fat:REAL, Examples: [35.68, 6.89, 7.24]),
(pcnt_cal_prot:REAL, Examples: [7.53, 16.17, 14.3]),
(calories:REAL, Examples: [290.79, 141.01, 137.06])
]
# Table: Recipe
[
(recipe_id:INTEGER, Primary Key, Examples: [214, 215, 216]),
(title:TEXT, Examples: [Raspberry Chiffon Pie]),
(subtitle:TEXT, Examples: [with Banana Cream Whip]),
(servings:INTEGER, Examples: [10, 4, 8]),
(yield_unit:TEXT, Examples: [1 pie, 8 tarts, 2 quarts]),
(prep_min:INTEGER, Examples: [20, 5, 10]),
(cook_min:INTEGER, Examples: [8, 2, 13]),
(stnd_min:INTEGER, Examples: [305, 65, 0]),
(source:TEXT, Examples: [The California Tree Fruit Agreement]),
(intro:TEXT),
(directions:TEXT)
]
# Table: Ingredient
[
(ingredient_id:INTEGER, Primary Key, Examples: [1, 6, 10]),
(category:TEXT, Examples: [dairy, Mexican products, cereals]),
(name:TEXT, Examples: [1% lowfat cottage cheese]),
(plural:TEXT, Examples: [s, #NAME?, es])
]
【Foreign keys】
Quantity.recipe_id=Recipe.recipe_id
Quantity.ingredient_id=Ingredient.ingredient_id
Quantity.recipe_id=Nutrition.recipe_id
Nutrition.recipe_id=Recipe.recipe_id

    【Question】
    Among all recipes containing cheese, what is the percentage of recipes with calories greater than 200? cheese is a category; calories greater than 200 refers to calories > 200; calculation = MULTIPLY(DIVIDE(COUNT(calories > 200 THEN recipe_id), COUNT(recipe_id)), 100)

    【Evidence】
    cheese is a category; calories greater than 200 refers to calories > 200; calculation = MULTIPLY(DIVIDE(COUNT(calories > 200 THEN recipe_id), COUNT(recipe_id)), 100)

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

SQL: SELECT CAST(SUM(CASE WHEN T4.calories > 200 THEN 1 ELSE 0 END) AS REAL) * 100 / COUNT(*) FROM Recipe AS T1 INNER JOIN Quantity AS T2 ON T1.recipe_id = T2.recipe_id INNER JOIN Ingredient AS T3 ON T3.ingredient_id = T2.ingredient_id INNER JOIN Nutrition AS T4 ON T4.recipe_id = T1.recipe_id WHERE T3.category = 'cheese'