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

    【Schema】
    【DB_ID】 address
【Schema】
# Table: country
[
(zip_code:INTEGER, Primary Key, Examples: [501, 544, 601]),
(county:TEXT, Primary Key, Examples: [SUFFOLK, ADJUNTAS, AGUADA]),
(state:TEXT, Examples: [NY, PR, VI])
]
# Table: CBSA
[
(CBSA:INTEGER, Primary Key, Examples: [10300, 10380, 10420]),
(CBSA_name:TEXT, Examples: [Adrian, MI]),
(CBSA_type:TEXT, Examples: [Micro, Metro])
]
# Table: zip_congress
[
(zip_code:INTEGER, Primary Key, Examples: [501, 601, 602]),
(district:TEXT, Primary Key, Examples: [NY-1, PR, VI])
]
# Table: congress
[
(cognress_rep_id:TEXT, Primary Key, Examples: [AK, AK-S1, AK-S2]),
(first_name:TEXT, Examples: [Young, Begich, Murkowski]),
(last_name:TEXT, Examples: [Don, Mark, Lisa]),
(CID:TEXT, Examples: [N00008091, N00009585, N00033101]),
(party:TEXT, Examples: [Republican, Democrat, Independent]),
(state:TEXT, Examples: [Alaska, Alabama, Arkansas]),
(abbreviation:TEXT, Examples: [AK, AL, AR]),
(House:TEXT, Examples: [House of Repsentatives]),
(District:INTEGER, Examples: [1, 2, 3]),
(land_area:REAL, Examples: [571951.26, 570641.0, 6066.83])
]
# Table: avoid
[
(zip_code:INTEGER, Primary Key, Examples: [501, 544, 601]),
(bad_alias:TEXT, Primary Key, Examples: [Internal Revenue Service])
]
# Table: area_code
[
(zip_code:INTEGER, Primary Key, Examples: [501, 544, 601]),
(area_code:INTEGER, Primary Key, Examples: [631, 787, 939])
]
# Table: alias
[
(zip_code:INTEGER, Primary Key, Examples: [501, 544, 601]),
(alias:TEXT, Examples: [Holtsville, Adjuntas, Aguada])
]
# Table: state
[
(abbreviation:TEXT, Primary Key, Examples: [AA, AE, AK]),
(name:TEXT, Examples: [Armed Forces Americas])
]
# Table: zip_data
[
(zip_code:INTEGER, Primary Key, Examples: [501, 544, 601]),
(city:TEXT, Examples: [Holtsville, Adjuntas, Aguada]),
(state:TEXT, Examples: [NY, PR, VI]),
(multi_county:TEXT, Examples: [No, Yes]),
(type:TEXT, Examples: [Unique Post Office, Post Office, P.O. Box Only]),
(organization:TEXT, Examples: [I R S Service Center]),
(time_zone:TEXT, Examples: [Eastern, Atlantic, Central]),
(daylight_savings:TEXT, Examples: [Yes, No]),
(latitude:REAL, Examples: [40.817923, 40.788827, 18.196747]),
(longitude:REAL, Examples: [-73.045317, -73.039405, -66.736735]),
(elevation:INTEGER, Examples: [25, 0, 70]),
(state_fips:INTEGER, Examples: [36, 72, 78]),
(county_fips:INTEGER, Examples: [103, 1, 3]),
(region:TEXT, Examples: [Northeast, South, Midwest]),
(division:TEXT, Examples: [Middle Atlantic, New England, South Atlantic]),
(population_2020:INTEGER, Examples: [0, 11737, 24263]),
(population_2010:INTEGER, Examples: [0, 18570, 41520]),
(households:INTEGER, Examples: [0, 6525, 15002]),
(avg_house_value:INTEGER, Examples: [0, 86200, 86300]),
(avg_income_per_household:INTEGER, Examples: [0, 13092, 16358]),
(persons_per_household:REAL, Examples: [0.0, 2.84, 2.76]),
(white_population:INTEGER, Examples: [0, 17479, 36828]),
(black_population:INTEGER, Examples: [0, 663, 2860]),
(hispanic_population:INTEGER, Examples: [0, 18486, 41265]),
(asian_population:INTEGER, Examples: [0, 7, 42]),
(american_indian_population:INTEGER, Examples: [0, 113, 291]),
(hawaiian_population:INTEGER, Examples: [0, 10, 32]),
(other_population:INTEGER, Examples: [0, 558, 2634]),
(male_population:INTEGER, Examples: [0, 9078, 20396]),
(female_population:INTEGER, Examples: [0, 9492, 21124]),
(median_age:REAL, Examples: [0.0, 35.9, 37.5]),
(male_median_age:REAL, Examples: [0.0, 34.5, 36.6]),
(female_median_age:REAL, Examples: [0.0, 37.1, 38.5]),
(residential_mailboxes:INTEGER, Examples: [0, 4133, 8791]),
(business_mailboxes:INTEGER, Examples: [1, 0, 221]),
(total_delivery_receptacles:INTEGER, Examples: [1, 0, 5173]),
(businesses:INTEGER, Examples: [2, 0, 472]),
(1st_quarter_payroll:INTEGER, Examples: [0, 88685000, 109167000]),
(annual_payroll:INTEGER, Examples: [0, 376492000, 472633000]),
(employees:INTEGER, Examples: [0, 9127, 18894]),
(water_area:REAL, Examples: [0.0, 0.309, 1.71]),
(land_area:REAL, Examples: [0.0, 64.348, 30.621]),
(single_family_delivery_units:INTEGER, Examples: [0, 2419, 5473]),
(multi_family_delivery_units:INTEGER, Examples: [0, 1264, 827]),
(total_beneficiaries:INTEGER, Examples: [0, 5135, 16805]),
(retired_workers:INTEGER, Examples: [0, 2940, 12145]),
(disabled_workers:INTEGER, Examples: [0, 885, 1835]),
(parents_and_widowed:INTEGER, Examples: [0, 155, 1045]),
(spouses:INTEGER, Examples: [0, 145, 1085]),
(children:INTEGER, Examples: [0, 1010, 695]),
(over_65:INTEGER, Examples: [0, 3245, 13260]),
(monthly_benefits_all:INTEGER, Examples: [0, 6137000, 14602000]),
(monthly_benefits_retired_workers:INTEGER, Examples: [0, 4007000, 10947000]),
(monthly_benefits_widowed:INTEGER, Examples: [0, 185000, 966000]),
(CBSA:INTEGER, Examples: [35620, 38660, 10380])
]
【Foreign keys】
country.zip_code=zip_data.zip_code
country.state=state.abbreviation
zip_congress.district=congress.cognress_rep_id
zip_congress.zip_code=zip_data.zip_code
congress.abbreviation=state.abbreviation
avoid.zip_code=zip_data.zip_code
area_code.zip_code=zip_data.zip_code
alias.zip_code=zip_data.zip_code
zip_data.state=state.abbreviation
zip_data.CBSA=CBSA.CBSA

    【Question】
    Which city has the most bad aliases? the most bad aliases refer to MAX(COUNT(bad_alias));

    【Evidence】
    the most bad aliases refer to MAX(COUNT(bad_alias));

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

SQL: SELECT T2.city FROM avoid AS T1 INNER JOIN zip_data AS T2 ON T1.zip_code = T2.zip_code GROUP BY T1.bad_alias ORDER BY COUNT(T1.zip_code) DESC LIMIT 1