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: "measuring the happiness of large scale written expression songs blogs and presidents"
  Abstract: The importance of quantifying the nature and intensity of emotional states at the level of populations is evident: we would like to know how, when, and why individuals feel as they do if we wish, for example, to better construct public policy, build more successful organizations, and, from a scientific perspective, more fully understand economic and social phenomena. Here, by incorporating direct human assessment of words, we quantify happiness levels on a continuous scale for a diverse set of large-scale texts: song titles and lyrics, weblogs, and State of the Union addresses. Our method is transparent, improvable, capable of rapidly processing Web-scale texts, and moves beyond approaches based on coarse categorization. Among a number of observations, we find that the happiness of song lyrics trends downward from the 1960s to the mid 1990s while remaining stable within genres, and that the happiness of blogs has steadily increased from 2005 to 2009, exhibiting a striking rise and fall with blogger age and distance from the Earth’s equator.

Connected through hyperedge HE-1 (induced by source paper "mining social media data for biomedical signals and health related behavior"):
  with members:
    - "singular value decomposition and neural networks"
    - "anatomy of an online misinformation network"
    - "human sexual cycles are driven by culture and match collective moods"
    - "limited individual attention and online virality of low quality information"
    - "quantified self meets social media sharing of weight updates on twitter"
    - "the rise of social bots"
    - "learning deep structure preserving image text embeddings"
    - "insights from machine learned diet success prediction"

Connected through hyperedge HE-2 (induced by source paper "frameaxis characterizing framing bias and intensity with word embedding"):
  with members:
    - "the geometry of culture analyzing the meanings of class through word embeddings"
    - "semaxis a lightweight framework to characterize domain specific word semantics beyond sentiment"
    - "framing and agenda setting in russian news a computational analysis of intricate political strategies"
    - "gendered conversation in a social game streaming platform"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-3 (induced by source paper "goodnewseveryone a corpus of news headlines annotated with emotions semantic roles and reader perception"):
  with members:
    - "po emo conceptualization annotation and modeling of aesthetic emotions in german and english poetry"
    - "emotion cause pair extraction a new task to emotion analysis in texts"
    - "utilizing bert for aspect based sentiment analysis via constructing auxiliary sentence"
    - "rthn a rnn transformer hierarchical network for emotion cause extraction"
    - "from independent prediction to re ordered prediction integrating relative position and global label information to emotion cause identification"
    - "frowning frodo wincing leia and a seriously great friendship learning to classify emotional relationships of fictional characters"
    - "crowdsourcing and validating event focused emotion corpora for german and english"
    - "modeling word emotion in historical language quantity beats supposed stability in seed word selection"

Connected through hyperedge HE-4 (induced by source paper "identifying long term periodic cycles and memories of collective emotion in online social media"):
  with members:
    - "measuring emotional contagion in social media"
    - "self organization on social media endo exo bursts and baseline fluctuations"
    - "distributed representations of sentences and documents"
    - "analysing mood patterns in the united kingdom through twitter content"
    - "distributed representations of words and phrases and their compositionality"
    - "panas t a pychometric scale for measuring sentiments on twitter"
    - "detecting events and patterns in large scale user generated textual streams with statistical learning methods"
    - "twitter mood predicts the stock market"

Connected through hyperedge HE-5 (induced by source paper "foundations of temporal text networks"):
  with members:
    - "detecting socio economic impact of cultural investment through geo social network analysis"
    - "graph embedding techniques applications and performance a survey"
    - "motifs in temporal networks"
    - "automated analysis of topic actor networks on twitter new approach to the analysis of socio semantic networks"
    - "when is a network a network multi order graphical model selection in pathways and temporal networks"
    - "modelling sequences and temporal networks with dynamic community structures"
    - "do cascades recur"
    - "clustering attributed graphs models measures and methods"

Connected through hyperedge HE-6 (induced by source paper "exposure to urban parks improves affect and reduces negativity on twitter"):
  with members:
    - "the emotional arcs of stories are dominated by six basic shapes"
    - "geo located twitter as proxy for global mobility patterns"

Connected through hyperedge HE-7 (induced by source paper "the minor fall the major lift inferring emotional valence of musical chords through lyrics"):
  with members:
    - "comparing and combining sentiment analysis methods"
    - "twitter reciprocal reply networks exhibit assortativity with respect to happiness"

Connected through hyperedge HE-8 (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"
    - "positive words carry less information than negative words"
    - "a new anew evaluation of a word list for sentiment analysis in microblogs"
    - "twitter mood predicts the stock market"
    - "a sentimental education sentiment analysis using subjectivity summarization based on minimum cuts"

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.SI(Social and Information Networks)