In this task, you are given a paragraph (from a research paper) and your task is to classify each sentence of the paragraph (assume n sentence) into the following categories: Background, Purpose, Method, Finding/Contribution, or Other. Return the output in this format: 1 - category of sentence 1, 2 - category of sentence 2, ..., n - category of sentence n; where each number indicates the order of the sentence. The categories can be identified using the following questions: 
 Background: - Why is this problem important? - What relevant works have been done before? - What did the previous works miss? - What are the high-level research questions? - How might this help other researchers?
 Purpose: - What specific things do the researchers want to do? - What specific knowledge do the researchers want to know? - What specific hypothesis do the researchers want to test?
 Method: - How did the researchers do it or find it out? - What are the procedures and steps of this research?
 Finding/Contribution: - What did the researchers find out? Did the proposed methods work? - Did things behave as the researchers expected?
Other: put every text fragment that does not fit into any of the categories above here. Put a sentence here if its not in English, is not part of the article, contains numbers and dates, is a caption of a figure or a table, is a formatting error, or you are not sure about it.

Example Input: Several studies have demonstrated that administration of EBOV GP-specific antibodies protects nonhuman primates from lethal EBOV infection and may constitute a leading treatment option for EVD in humans [12] [13] [14] [15] [16] . During the West African EVD outbreak, EBOV GP-specific monoclonal antibody (MAb) cocktails (e.g., ZMapp) 12 , were used to treat several patients 17-20 . However, most of characterized therapeutic MAbs to date are EBOV GP-specific and cross-neutralizing activity against any other ebolavirus species (e.g., SUDV and
Example Output: 1 - background, 2 - background, 3 - finding, 4 - background, 5 - background

Example Input: Background: From October 2010 through February 2016, Arizona conducted surveillance for severe acute respiratory infections (SARI) among adults hospitalized in the Arizona-Mexico border region. There are few accurate mortality estimates in SARI patients, particularly in adults ≥ 65 years old. The purpose of this study was to generate mortality estimates among SARI patients that include deaths occurring shortly after hospital discharge and identify risk factors for mortality. Methods: Patients admitted to two sentinel hospitals between 2010 and 2014 who met the SARI case definition were enrolled. Demographic data were used to link SARI patients to Arizona death certificates. Mortality within 30 days after the date of admission was calculated and risk factors were identified using logistic regression models. Results: Among 258 SARI patients, 47% were females, 51% were white, non-Hispanic and 39% were Hispanic. The median age was 63 years (range, 19 to 97 years) and 80% had one or more pre-existing health condition; 9% died in hospital. Mortality increased to 12% (30/258, 30% increase) when electronic vital records and a 30-day posthospitalization time frame were used. Being age ≥ 65 years (OR = 4.0; 95% CI: 1.6-9.9) and having an intensive care unit admission (OR = 7.4; 95% CI: 3.0-17.9) were independently associated with mortality. Conclusion: The use of electronic vital records increased SARI-associated mortality estimates by 30%. These findings may help guide prevention and treatment measures, particularly in high-risk persons in this highly fluid border population.
Example Output: 1 - background, 2 - background, 3 - background, 4 - background, 5 - purpose, 6 - method, 7 - method, 8 - method, 9 - finding, 10 - finding, 11 - finding, 12 - finding, 13 - finding, 14 - finding, 15 - finding, 16 - finding, 17 - finding

Example Input: 0 1992 Academic PVE.S. Inc.
Example Output:
1 - other, 2 - other