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: "emoticons vs emojis on twitter a causal inference approach"
  Abstract: Online writing lacks the non-verbal cues present in face-to-face communication, which provide additional contextual information about the utterance, such as the speaker's intention or affective state. To fill this void, a number of orthographic features, such as emoticons, expressive lengthening, and non-standard punctuation, have become popular in social media services including Twitter and Instagram. Recently, emojis have been introduced to social media, and are increasingly popular. This raises the question of whether these predefined pictographic characters will come to replace earlier orthographic methods of paralinguistic communication. In this abstract, we attempt to shed light on this question, using a matching approach from causal inference to test whether the adoption of emojis causes individual users to employ fewer emoticons in their text on Twitter.

Connected through hyperedge HE-1 (induced by source paper "estimating causal effects of tone in online debates"):
  with members:
    - "before name calling dynamics and triggers of ad hominem fallacies in web argumentation"
    - "topic independent identification of agreement and disagreement in social media dialogue"
    - "really well apparently bootstrapping improves the performance of sarcasm and nastiness classifiers for online dialogue"
    - "winning arguments interaction dynamics and persuasion strategies in good faith online discussions"
    - "antisocial behavior in online discussion communities"
    - "using summarization to discover argument facets in online idealogical dialog"
    - "the effect of wording on message propagation topic and author controlled natural experiments on twitter"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-2 (induced by source paper "i stand with you using emojis to study solidarity in crisis events"):
  with members:
    - "spice up your chat the intentions and sentiment effects of using emoji"
    - "emojinet building a machine readable sense inventory for emoji"
    - "sentiment of emojis"

Connected through hyperedge HE-3 (induced by source paper "ermes emoji powered representation learning for cross lingual sentiment classification"):
  with members:
    - "deep learning for sentiment analysis a survey"
    - "through a gender lens learning usage patterns of emojis from large scale android users"
    - "spice up your chat the intentions and sentiment effects of using emoji"
    - "leveraging large amounts of weakly supervised data for multi language sentiment classification"
    - "multitask learning for fine grained twitter sentiment analysis"
    - "tensorflow a system for large scale machine learning"
    - "adam a method for stochastic optimization"
    - "distributed representations of sentences and documents"

Connected through hyperedge HE-4 (induced by source paper "twitter sentiment analysis via bi sense emoji embedding and attention based lstm"):
  with members:
    - "mining the relationship between emoji usage patterns and personality"
    - "bb_twtr at semeval 2017 task 4 twitter sentiment analysis with cnns and lstms"
    - "spice up your chat the intentions and sentiment effects of using emoji"
    - "sense embedding learning for word sense induction"
    - "emoji2vec learning emoji representations from their description"
    - "enriching word vectors with subword information"
    - "skip thought vectors"
    - "sentiment of emojis"

Connected through hyperedge HE-5 (induced by source paper "through a gender lens learning usage patterns of emojis from large scale android users"):
  with members:
    - "spice up your chat the intentions and sentiment effects of using emoji"
    - "emoji2vec learning emoji representations from their description"
    - "inferring gender from names on the web a comparative evaluation of gender detection methods"
    - "line large scale information network embedding"
    - "sentiment of emojis"
    - "privacy tradeoffs in predictive analytics"

Connected through hyperedge HE-6 (induced by source paper "a first look at emoji usage on github an empirical study"):
  with members:
    - "through a gender lens learning usage patterns of emojis from large scale android users"
    - "twitter sentiment analysis via bi sense emoji embedding and attention based lstm"
    - "spice up your chat the intentions and sentiment effects of using emoji"
    - "modelling politics in requirements engineering adding emoji to existing notations"
    - "sentiment of emojis"
    - "the role of emotions in contributors activity a case study on the gentoo community"

Connected through hyperedge HE-7 (induced by source paper "cross platform emoji interpretation analysis a solution and applications"):
  with members:
    - "slangsd building and using a sentiment dictionary of slang words for short text sentiment classification"
    - "emoji2vec learning emoji representations from their description"
    - "distributed representations of words and phrases and their compositionality"
    - "crowdsourcing a word emotion association lexicon"

Connected through hyperedge HE-8 (induced by source paper "spice up your chat the intentions and sentiment effects of using emoji"):
  with members:
    - "emojinet building a machine readable sense inventory for emoji"
    - "emoji2vec learning emoji representations from their description"

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)