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: "sentiment of emojis"
  Abstract: There is a new generation of emoticons, called emojis, that is increasingly being used in mobile communications and social media. In the past two years, over ten billion emojis were used on Twitter. Emojis are Unicode graphic symbols, used as a shorthand to express concepts and ideas. In contrast to the small number of well-known emoticons that carry clear emotional contents, there are hundreds of emojis. But what are their emotional contents? We provide the first emoji sentiment lexicon, called the Emoji Sentiment Ranking, and draw a sentiment map of the 751 most frequently used emojis. The sentiment of the emojis is computed from the sentiment of the tweets in which they occur. We engaged 83 human annotators to label over 1.6 million tweets in 13 European languages by the sentiment polarity (negative, neutral, or positive). About 4% of the annotated tweets contain emojis. The sentiment analysis of the emojis allows us to draw several interesting conclusions. It turns out that most of the emojis are positive, especially the most popular ones. The sentiment distribution of the tweets with and without emojis is significantly different. The inter-annotator agreement on the tweets with emojis is higher. Emojis tend to occur at the end of the tweets, and their sentiment polarity increases with the distance. We observe no significant differences in the emoji rankings between the 13 languages and the Emoji Sentiment Ranking. Consequently, we propose our Emoji Sentiment Ranking as a European language-independent resource for automated sentiment analysis. Finally, the paper provides a formalization of sentiment and a novel visualization in the form of a sentiment bar.

Connected through hyperedge HE-1 (induced by source paper "vocabulary based method for quantifying controversy in social media"):
  with members:
    - "quantifying polarization on twitter the kavanaugh nomination"
    - "community interaction and conflict on the web"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "joint non negative matrix factorization for learning ideological leaning on twitter"
    - "a distance measure for the analysis of polar opinion dynamics in social networks"
    - "characterizing information diets of social media users"
    - "using word embeddings in twitter election classification"
    - "reducing controversy by connecting opposing views"

Connected through hyperedge HE-2 (induced by source paper "multimodal emotion classification"):
  with members:
    - "what s ur type contextualized classification of user types in marijuana related communications using compositional multiview embedding"
    - "which emoji talks best for my picture"
    - "learning emoji embeddings using emoji co occurrence network graph"
    - "multimodal emoji prediction"
    - "a semantics based measure of emoji similarity"
    - "fasttext zip compressing text classification models"
    - "emojinet an open service and api for emoji sense discovery"
    - "finding street gang members on twitter"

Connected through hyperedge HE-3 (induced by source paper "smile be happy emoji embedding for visual sentiment analysis"):
  with members:
    - "i stand with you using emojis to study solidarity in crisis events"
    - "the new modality emoji challenges in prediction anticipation and retrieval"
    - "building emotional machines recognizing image emotions through deep neural networks"
    - "recovering the missing link predicting class attribute associations for unsupervised zero shot learning"
    - "emoji2vec learning emoji representations from their description"
    - "building a large scale dataset for image emotion recognition the fine print and the benchmark"
    - "from pixels to sentiment fine tuning cnns for visual sentiment prediction"
    - "robust image sentiment analysis using progressively trained and domain transferred deep networks"

Connected through hyperedge HE-4 (induced by source paper "um iu ling at semeval 2019 task 6 identifying offensive tweets using bert and svms"):
  with members:
    - "predicting the type and target of offensive posts in social media"
    - "semeval 2019 task 6 identifying and categorizing offensive language in social media offenseval"
    - "challenges in discriminating profanity from hate speech"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "large scale crowdsourcing and characterization of twitter abusive behavior"
    - "automated hate speech detection and the problem of offensive language"
    - "deep learning for hate speech detection in tweets"

Connected through hyperedge HE-5 (induced by source paper "tax2vec constructing interpretable features from taxonomies for short text classification"):
  with members:
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "struc2vec learning node representations from structural identity"
    - "prototypical networks for few shot learning"
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"
    - "deep learning in neural networks"
    - "how much data is needed to train a medical image deep learning system to achieve necessary high accuracy"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-6 (induced by source paper "reconviguration reconfiguring physical keyboards in virtual reality"):
  with members:
    - "security and privacy approaches in mixed reality a literature survey"
    - "text entry in immersive head mounted display based virtual reality using standard keyboards"
    - "effects of hand representations for typing in virtual reality"
    - "keyboard surface interaction making the keyboard into a pointing device"

Connected through hyperedge HE-7 (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"
    - "emoticons vs emojis on twitter a causal inference approach"

Connected through hyperedge HE-8 (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"
    - "emoticons vs emojis on twitter a causal inference approach"

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)