human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "a benchmark comparison of state of the practice sentiment analysis methods"):
  Abstract: In the last few years thousands of scientific papers have explored sentiment analysis, several startups that measures opinions on real data have emerged, and a number of innovative products related to this theme have been developed. There are multiple methods for measuring sentiments, including lexical-based approaches and supervised machine learning methods. Despite the vast interest on the theme and wide popularity of some methods, it is unclear which method is better for identifying the polarity (i.e., positive or negative) of a message. Thus, there is a strong need to conduct a thorough apple-to-apple comparison of sentiment analysis methods, as they are used in practice, across multiple datasets originated from different data sources. Such a comparison is key for understanding the potential limitations, advantages, and disadvantages of popular methods. This study aims at filling this gap by presenting a benchmark comparison of twenty one popular sentiment analysis methods (which we call the state-of-the-practice methods). Our evaluation is based on a benchmark of twenty labeled datasets, covering messages posted on social networks, movie and product reviews, as well as opinions and comments in news articles. Our results highlight the extent to which the prediction performance of these methods varies widely across datasets. Aiming at boosting the development of this research area, we open the methods' codes and datasets used in this paper and we deploy a benchmark system, which provides an open API for accessing and comparing sentence-level sentiment analysis methods.

Includes members:
  Member "effective use of word order for text categorization with convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a unified system for aggression identification in english code mixed and uni lingual texts")
  Member "a convolutional neural network for modelling sentences":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis")
  Member "nrc canada building the state of the art in sentiment analysis of tweets":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "embedding projection for targeted cross lingual sentiment model comparisons and a real world study")
  Member "crowdsourcing a word emotion association lexicon":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "text classification with lexicon from preattention mechanism")
  Member "positive words carry less information than negative words":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "the qwerty effect on the web how typing shapes the meaning of words in online human computer interaction")
  Member "a new anew evaluation of a word list for sentiment analysis in microblogs":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "how impersonators exploit instagram to generate fake engagement")
  Member "twitter mood predicts the stock market":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "mining social media data for biomedical signals and health related behavior")
  Member "measuring the happiness of large scale written expression songs blogs and presidents":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "mining social media data for biomedical signals and health related behavior")

Question: Please tell me which class the source paper belongs to. The 40 classes are: cs.NA(Numerical Analysis), cs.MM(Multimedia), cs.LO(Logic in Computer Science), cs.CY(Computers and Society), cs.CR(Cryptography and Security), cs.DC(Distributed, Parallel, and Cluster Computing), cs.HC(Human-Computer Interaction), cs.CE(Computational Engineering, Finance, and Science), cs.NI(Networking and Internet Architecture), cs.CC(Computational Complexity), cs.AI(Artificial Intelligence), cs.MA(Multiagent Systems), cs.GL(General Literature), cs.NE(Neural and Evolutionary Computing), cs.SC(Symbolic Computation), cs.AR(Hardware Architecture), cs.CV(Computer Vision and Pattern Recognition), cs.GR(Graphics), cs.ET(Emerging Technologies), cs.SY(Systems and Control), cs.CG(Computational Geometry), cs.OH(Other Computer Science), cs.PL(Programming Languages), cs.SE(Software Engineering), cs.LG(Machine Learning), cs.SD(Sound), cs.SI(Social and Information Networks), cs.RO(Robotics), cs.IT(Information Theory), cs.PF(Performance), cs.CL(Computation and Language), cs.IR(Information Retrieval), cs.MS(Mathematical Software), cs.FL(Formal Languages and Automata Theory), cs.DS(Data Structures and Algorithms), cs.OS(Operating Systems), cs.GT(Computer Science and Game Theory), cs.DB(Databases), cs.DL(Digital Libraries), cs.DM(Discrete Mathematics). Directly output the class name.

gpt: cs.CL(Computation and Language)