system: You are an expert security code reviewer. You will be shown a source code file along with a catalog of candidate CWE (Common Weakness Enumeration) categories with their official MITRE names. Your task is to list every distinct CWE from that catalog that is actually present in the code. The catalog is comprehensive for this dataset -- the answer is a (possibly small) subset of it.

OUTPUT FORMAT: Respond with a comma-separated list of CWE IDs in the form `CWE-N`, sorted in ascending numeric order, wrapped in a single \boxed{...}. Examples: `\boxed{CWE-79}` for one, `\boxed{CWE-22,CWE-79,CWE-327}` for three. Output the boxed answer ONLY -- no explanation, no other text.

user: ```python
import csv
from numpy import array, asarray, zeros
from keras.preprocessing.text import one_hot, Tokenizer
from keras.preprocessing.sequence import pad_sequences
from keras.models import Sequential
from keras.layers import Dense, SpatialDropout1D, LSTM
from keras.layers import Flatten
from keras.layers.embeddings import Embedding
import pandas as pd
import re
from sklearn.model_selection import train_test_split

# read input document
X = pd.read_csv('/Users/manpreet.singh/git/deeplearning/deeplearning-keras/ch08/sentiment-analysis/Sentiment.csv')
X = X[['text', 'sentiment']]
X = X[X.sentiment != 'Neutral']
X['text'] = X['text'].apply(lambda x: x.lower())
X['text'] = X['text'].apply((lambda x: re.sub('[^a-zA-z0-9\s]', '', x)))
print(X)

for idx, row in X.iterrows():
    row[0] = row[0].replace('rt', ' ')

print(type(X))

# use tokenizer and pad
maxFeatures = 2000
tokenizer = Tokenizer(num_words=maxFeatures, split=' ')
tokenizer.fit_on_texts(X['text'].values)
encodeDocuments = tokenizer.texts_to_sequences(X['text'].values)
print(encodeDocuments)

max_length = 29
paddedDocuments = pad_sequences(encodeDocuments, maxlen=max_length, padding='post')

# load glove model
inMemoryGlove = dict()
f = open('/Users/manpreet.singh/git/deeplearning/deeplearning-keras/ch08/embeddings/glove.6B.100d.txt')
for line in f:
    values = line.split()
    word = values[0]
    coefficients = asarray(values[1:], dtype='float32')
    inMemoryGlove[word] = coefficients
f.close()
print(len(inMemoryGlove))

# split data
labels = []
for i in X['sentiment']:
    if i == 'Positive':
        labels.append(1)
    else:
        labels.append(0)

labels = array(labels)

X_train, X_test, Y_train, Y_test = train_test_split(paddedDocuments,labels, test_size = 0.33, random_state = 42)
print(X_train.shape,Y_train.shape)
print(X_test.shape,Y_test.shape)


# create coefficient matrix for training data
trainingToEmbeddings = zeros((maxFeatures, 100))
for word, i in tokenizer.word_index.items():
    if i < 2001:
        gloveVector = inMemoryGlove.get(word)
        if gloveVector is not None:
            trainingToEmbeddings[i] = gloveVector

model = Sequential()
model.add(Embedding(maxFeatures, 100, weights=[trainingToEmbeddings], input_length=max_length, trainable=False))
model.add(Flatten())
model.add(Dense(1, activation='sigmoid'))
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['acc'])
print(model.summary())

batch_size = 32
model.fit(X_train, Y_train, epochs=50, batch_size=batch_size, verbose=0)

loss, accuracy = model.evaluate(X_test, Y_test, verbose=0)
print('Accuracy: %f' % (accuracy * 100))
```

Candidate CWE catalog (every CWE that appears anywhere in this dataset):
  - CWE-20: Improper Input Validation
  - CWE-22: Improper Limitation of a Pathname to a Restricted Directory ('Path Traversal')
  - CWE-74: Improper Neutralization of Special Elements in Output Used by a Downstream Component ('Injection')
  - CWE-78: Improper Neutralization of Special Elements used in an OS Command ('OS Command Injection')
  - CWE-79: Improper Neutralization of Input During Web Page Generation ('Cross-site Scripting')
  - CWE-89: Improper Neutralization of Special Elements used in an SQL Command ('SQL Injection')
  - CWE-94: Improper Control of Generation of Code ('Code Injection')
  - CWE-113: Improper Neutralization of CRLF Sequences in HTTP Headers ('HTTP Request/Response Splitting')
  - CWE-116: Improper Encoding or Escaping of Output
  - CWE-117: Improper Output Neutralization for Logs
  - CWE-200: Exposure of Sensitive Information to an Unauthorized Actor
  - CWE-209: Generation of Error Message Containing Sensitive Information
  - CWE-215: Insertion of Sensitive Information Into Debugging Code
  - CWE-295: Improper Certificate Validation
  - CWE-312: Cleartext Storage of Sensitive Information
  - CWE-326: Inadequate Encryption Strength
  - CWE-327: Use of a Broken or Risky Cryptographic Algorithm
  - CWE-377: Insecure Temporary File
  - CWE-502: Deserialization of Untrusted Data
  - CWE-601: URL Redirection to Untrusted Site ('Open Redirect')
  - CWE-611: Improper Restriction of XML External Entity Reference
  - CWE-614: Sensitive Cookie in HTTPS Session Without 'Secure' Attribute
  - CWE-643: Improper Neutralization of Data within XPath Expressions ('XPath Injection')
  - CWE-730: OWASP Top Ten 2004 Category A9 - Denial of Service
  - CWE-732: Incorrect Permission Assignment for Critical Resource
  - CWE-776: Improper Restriction of Recursive Entity References in DTDs ('XML Entity Expansion')
  - CWE-918: Server-Side Request Forgery (SSRF)
  - CWE-943: Improper Neutralization of Special Elements in Data Query Logic
  - CWE-1004: Sensitive Cookie Without 'HttpOnly' Flag
  - CWE-1333: Inefficient Regular Expression Complexity

List every distinct CWE from the catalog above that is actually present in this code.
Answer: CWE-20