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: "positive words carry less information than negative words"
  Abstract: We show that the frequency of word use is not only determined by the word length [1] and the average information content [2], but also by its emotional content. We have analyzed three established lexica of affective word usage in English, German, and Spanish, to verify that these lexica have a neutral, unbiased, emotional content. Taking into account the frequency of word usage, we find that words with a positive emotional content are more frequently used. This lends support to Pollyanna hypothesis [3] that there should be a positive bias in human expression. We also find that negative words contain more information than positive words, as the informativeness of a word increases uniformly with its valence decrease. Our findings support earlier conjectures about (i) the relation between word frequency and information content, and (ii) the impact of positive emotions on communication and social links.

Connected through hyperedge HE-1 (induced by source paper "the qwerty effect on the web how typing shapes the meaning of words in online human computer interaction"):
  with members:
    - "inferring networks of substitutable and complementary products"
    - "image based recommendations on styles and substitutes"
    - "sentiment cascades in the 15m movement"
    - "social signals and algorithmic trading of bitcoin"
    - "who watches and shares what on youtube and when using twitter to understand youtube viewership"
    - "gender asymmetries in reality and fiction the bechdel test of social media"
    - "from amateurs to connoisseurs modeling the evolution of user expertise through online reviews"
    - "happiness is assortative in online social networks"

Connected through hyperedge HE-2 (induced by source paper "quantifying the effect of sentiment on information diffusion in social media"):
  with members:
    - "the rise of social bots"
    - "measuring emotional contagion in social media"
    - "on predictability of rare events leveraging social media a machine learning perspective"
    - "on facebook most ties are weak"
    - "evolution of online user behavior during a social upheaval"
    - "can cascades be predicted"
    - "romantic partnerships and the dispersion of social ties a network analysis of relationship status on facebook"
    - "predicting successful memes using network and community structure"

Connected through hyperedge HE-3 (induced by source paper "measuring emotional contagion in social media"):
  with members:
    - "on predictability of rare events leveraging social media a machine learning perspective"
    - "the simple rules of social contagion"
    - "quantifying the effect of sentiment on information diffusion in social media"
    - "on facebook most ties are weak"
    - "evolution of online user behavior during a social upheaval"
    - "can cascades be predicted"
    - "romantic partnerships and the dispersion of social ties a network analysis of relationship status on facebook"
    - "the digital evolution of occupy wall street"

Connected through hyperedge HE-4 (induced by source paper "a benchmark comparison of state of the practice sentiment analysis methods"):
  with members:
    - "effective use of word order for text categorization with convolutional neural networks"
    - "a convolutional neural network for modelling sentences"
    - "nrc canada building the state of the art in sentiment analysis of tweets"
    - "crowdsourcing a word emotion association lexicon"
    - "a new anew evaluation of a word list for sentiment analysis in microblogs"
    - "twitter mood predicts the stock market"
    - "measuring the happiness of large scale written expression songs blogs and presidents"
    - "a sentimental education sentiment analysis using subjectivity summarization based on minimum cuts"

Connected through hyperedge HE-5 (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"
    - "comparing and combining sentiment analysis methods"
    - "the role of social networks in information diffusion"

Connected through hyperedge HE-6 (induced by source paper "social ties and emotions evidence from social media"):
  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"
    - "comparing and combining sentiment analysis methods"

Connected through hyperedge HE-7 (induced by source paper "geography of emotion where in a city are people happier"):
  with members:
    - "measuring urban deprivation from user generated content"
    - "sentiment cascades in the 15m movement"
    - "big questions for social media big data representativeness validity and other methodological pitfalls"
    - "comparing and combining sentiment analysis methods"

Connected through hyperedge HE-8 (induced by source paper "gender asymmetries in reality and fiction the bechdel test of social media"):
  with members:
    - "who watches and shares what on youtube and when using twitter to understand youtube viewership"
    - "chameleons in imagined conversations a new approach to understanding coordination of linguistic style in dialogs"
    - "happiness is assortative in online social networks"
    - "mark my words linguistic style accommodation in social media"

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)