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: "the effect of wording on message propagation topic and author controlled natural experiments on twitter"
  Abstract: Consider a person trying to spread an important message on a social network. He/she can spend hours trying to craft the message. Does it actually matter? While there has been extensive prior work looking into predicting popularity of social-media content, the effect of wording per se has rarely been studied since it is often confounded with the popularity of the author and the topic. To control for these confounding factors, we take advantage of the surprising fact that there are many pairs of tweets containing the same url and written by the same user but employing different wording. Given such pairs, we ask: which version attracts more retweets? This turns out to be a more difficult task than predicting popular topics. Still, humans can answer this question better than chance (but far from perfectly), and the computational methods we develop can do better than both an average human and a strong competing method trained on non-controlled data.

Connected through hyperedge HE-1 (induced by source paper "why is chicago deceptive towards building model driven tutorials for humans"):
  with members:
    - "explaining machine learning classifiers through diverse counterfactual explanations"
    - "on human predictions with explanations and predictions of machine learning models a case study on deception detection"
    - "a human grounded evaluation of shap for alert processing"
    - "rating reliability and bias in news articles does ai assistance help everyone"
    - "human centered tools for coping with imperfect algorithms during medical decision making"
    - "efficient search for diverse coherent explanations"
    - "local rule based explanations of black box decision systems"
    - "it s reducing a human being to a percentage perceptions of justice in algorithmic decisions"

Connected through hyperedge HE-2 (induced by source paper "topics to avoid demoting latent confounds in text classification"):
  with members:
    - "attention is not not explanation"
    - "saliency driven word alignment interpretation for neural machine translation"
    - "is attention interpretable"
    - "attention is not explanation"
    - "examining gender and race bias in two hundred sentiment analysis systems"
    - "feature wise bias amplification"
    - "native language cognate effects on second language lexical choice"
    - "adversarial removal of demographic attributes from text data"

Connected through hyperedge HE-3 (induced by source paper "determining relative argument specificity and stance for complex argumentative structures"):
  with members:
    - "domain agnostic real valued specificity prediction"
    - "exploring the role of prior beliefs for argument persuasion"
    - "neural argument generation augmented with externally retrieved evidence"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "predicting specificity in classroom discussion"
    - "debbie the debate bot of the future"
    - "argument mining with structured svms and rnns"
    - "neural network based abstract generation for opinions and arguments"

Connected through hyperedge HE-4 (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"
    - "emoticons vs emojis on twitter a causal inference approach"
    - "using summarization to discover argument facets in online idealogical dialog"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-5 (induced by source paper "intergroup contact in the wild characterizing language differences between intergroup and single group members in nba related discussion forums"):
  with members:
    - "this is why we play characterizing online fan communities of the nba teams"
    - "community interaction and conflict on the web"
    - "extracting inter community conflicts in reddit"
    - "me my echo chamber and i introspection on social media polarization"
    - "automated hate speech detection and the problem of offensive language"
    - "anyone can become a troll causes of trolling behavior in online discussions"
    - "all who wander on the prevalence and characteristics of multi community engagement"
    - "antisocial behavior in online discussion communities"

Connected through hyperedge HE-6 (induced by source paper "an unsupervised domain independent framework for automated detection of persuasion tactics in text"):
  with members:
    - "event outcome prediction using sentiment analysis and crowd wisdom in microblog feeds"
    - "object sensitive deep reinforcement learning"
    - "transparency and explanation in deep reinforcement learning neural networks"
    - "photorealistic style transfer for videos"
    - "argument strength is in the eye of the beholder audience effects in persuasion"
    - "joint embedding of hierarchical categories and entities for concept categorization and dataless classification"
    - "winning arguments interaction dynamics and persuasion strategies in good faith online discussions"
    - "content based video indexing and retrieval using corr lda"

Connected through hyperedge HE-7 (induced by source paper "causal effects of brevity on style and success in social media"):
  with members:
    - "how constraints affect content the case of twitter s switch from 140 to 280 characters"
    - "anyone can become a troll causes of trolling behavior in online discussions"
    - "neural paraphrase generation with stacked residual lstm networks"
    - "tgsum build tweet guided multi document summarization dataset"
    - "a computational approach to politeness with application to social factors"
    - "exploring text virality in social networks"
    - "for the sake of simplicity unsupervised extraction of lexical simplifications from wikipedia"

Connected through hyperedge HE-8 (induced by source paper "the trumpiest trump identifying a subject s most characteristic tweets"):
  with members:
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "author identification using multi headed recurrent neural networks"
    - "seismic a self exciting point process model for predicting tweet popularity"
    - "adam a method for stochastic optimization"
    - "a bayesian approach for predicting the popularity of tweets"
    - "the pulse of news in social media forecasting popularity"

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)