human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "comparing and combining sentiment analysis methods"
  Abstract: Several messages express opinions about events, products, and services, political views or even their author's emotional state and mood. Sentiment analysis has been used in several applications including analysis of the repercussions of events in social networks, analysis of opinions about products and services, and simply to better understand aspects of social communication in Online Social Networks (OSNs). There are multiple methods for measuring sentiments, including lexical-based approaches and supervised machine learning methods. Despite the wide use and popularity of some methods, it is unclear which method is better for identifying the polarity (i.e., positive or negative) of a message as the current literature does not provide a method of comparison among existing methods. Such a comparison is crucial for understanding the potential limitations, advantages, and disadvantages of popular methods in analyzing the content of OSNs messages. Our study aims at filling this gap by presenting comparisons of eight popular sentiment analysis methods in terms of coverage (i.e., the fraction of messages whose sentiment is identified) and agreement (i.e., the fraction of identified sentiments that are in tune with ground truth). We develop a new method that combines existing approaches, providing the best coverage results and competitive agreement. We also present a free Web service called iFeel, which provides an open API for accessing and comparing results across different sentiment methods for a given text.

Connected through hyperedge HE-1 (induced by source paper "modeling spatiotemporal factors associated with sentiment on twitter synthesis and suggestions for improving the identification of localized deviations"):
  with members:
    - "the happiness paradox your friends are happier than you"
    - "a survey of location inference techniques on twitter"
    - "what a nasty day exploring mood weather relationship from twitter"
    - "home location identification of twitter users"
    - "geotagging one hundred million twitter accounts with total variation minimization"
    - "the dynamics of health behavior sentiments on a large online social network"
    - "assessing vaccination sentiments with online social media implications for infectious disease dynamics and control"
    - "twitter mood predicts the stock market"

Connected through hyperedge HE-2 (induced by source paper "coloring in the links capturing social ties as they are perceived"):
  with members:
    - "evolution of conversations in the age of email overload"
    - "reading the source code of social ties"
    - "collective phenomena and non finite state computation in a human social system"
    - "characterizing and curating conversation threads expansion focus volume re entry"
    - "echoes of power language effects and power differences in social interaction"
    - "the slashdot zoo mining a social network with negative edges"

Connected through hyperedge HE-3 (induced by source paper "using social media to predict the future a systematic literature review"):
  with members:
    - "what the language you tweet says about your occupation"
    - "mining online social data for detecting social network mental disorders"
    - "sentiment aggregate functions for political opinion polling using microblog streams"
    - "using user generated online photos to estimate and monitor air pollution in major cities"
    - "beating the news with embers forecasting civil unrest using open source indicators"
    - "predicting crowd behavior with big public data"
    - "big questions for social media big data representativeness validity and other methodological pitfalls"
    - "geotagging one hundred million twitter accounts with total variation minimization"

Connected through hyperedge HE-4 (induced by source paper "10sent a stable sentiment analysis method based on the combination of off the shelf approaches"):
  with members:
    - "deep learning for sentiment analysis a survey"
    - "leveraging large amounts of weakly supervised data for multi language sentiment classification"
    - "crowdsourcing a word emotion association lexicon"
    - "wikisent weakly supervised sentiment analysis through extractive summarization with wikipedia"
    - "a new anew evaluation of a word list for sentiment analysis in microblogs"
    - "twitter mood predicts the stock market"
    - "thumbs up sentiment classification using machine learning techniques"

Connected through hyperedge HE-5 (induced by source paper "i would not plant apple trees if the world will be wiped analyzing hundreds of millions of behavioral records of players during an mmorpg beta test"):
  with members:
    - "exploring cyberbullying and other toxic behavior in team competition online games"
    - "linguistic analysis of toxic behavior in an online video game"
    - "stfu noob predicting crowdsourced decisions on toxic behavior in online games"
    - "modeling epidemic spread in synthetic populations virtual plagues in massively multiplayer online games"

Connected through hyperedge HE-6 (induced by source paper "the minor fall the major lift inferring emotional valence of musical chords through lyrics"):
  with members:
    - "twitter reciprocal reply networks exhibit assortativity with respect to happiness"
    - "measuring the happiness of large scale written expression songs blogs and presidents"

Connected through hyperedge HE-7 (induced by source paper "the dynamics of emotions in online interaction"):
  with members:
    - "sentiment cascades in the 15m movement"

Connected through hyperedge HE-8 (induced by source paper "emotions demographics and sociability in twitter interactions"):
  with members:
    - "measuring urban deprivation from user generated content"
    - "user effort and network structure mediate access to information in networks"
    - "cultures in community question answering"
    - "sentiment cascades in the 15m movement"
    - "big questions for social media big data representativeness validity and other methodological pitfalls"
    - "the role of social networks in information diffusion"
    - "positive words carry less information than negative words"

Question: Please tell me which class the center node 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)