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: "identifying the social signals that drive online discussions a case study of reddit communities"
  Abstract: Increasingly people form opinions based on information they consume on online social media. As a result, it is crucial to understand what type of content attracts people's attention on social media and drive discussions. In this paper we focus on online discussions. Can we predict which comments and what content gets the highest attention in an online discussion? How does this content differ from community to community? To accomplish this, we undertake a unique study of Reddit involving a large sample comments from 11 popular subreddits with different properties. We introduce a large number of sentiment, relevance, content analysis features including some novel features customized to reddit. Through a comparative analysis of the chosen subreddits, we show that our models are correctly able to retrieve top replies under a post with great precision. In addition, we explain our findings with a detailed analysis of what distinguishes high scoring posts in different communities that differ along the dimensions of the specificity of topic and style, audience and level of moderation.

Connected through hyperedge HE-1 (induced by source paper "detecting toxicity in news articles application to bulgarian"):
  with members:
    - "hunting for troll comments in news community forums"
    - "in search of credible news"
    - "multi task ordinal regression for jointly predicting the trustworthiness and the leading political ideology of news media"
    - "predicting the leading political ideology of youtube channels using acoustic textual and metadata information"
    - "fact checking meets fauxtography verifying claims about images"
    - "fine grained analysis of propaganda in news articles"
    - "a retrospective analysis of the fake news challenge stance detection task"
    - "all in one multi task learning for rumour verification"

Connected through hyperedge HE-2 (induced by source paper "modeling engagement dynamics of online discussions using relativistic gravitational theory"):
  with members:
    - "deep reinforcement learning with a combinatorial action space for predicting and tracking popular discussion threads"
    - "scalable temporal latent space inference for link prediction in dynamic social networks"
    - "deep reinforcement learning with a combinatorial action space for predicting popular reddit threads"
    - "node2vec scalable feature learning for networks"
    - "winning arguments interaction dynamics and persuasion strategies in good faith online discussions"
    - "an ensemble method to produce high quality word embeddings"
    - "adam a method for stochastic optimization"
    - "distributed representations of sentences and documents"

Connected through hyperedge HE-3 (induced by source paper "the anatomy of reddit an overview of academic research"):
  with members:
    - "modelling structure and predicting dynamics of discussion threads in online boards"
    - "the aftermath of disbanding an online hateful community"
    - "tracing community genealogy how new communities emerge from the old"
    - "community interaction and conflict on the web"
    - "caveat emptor computational social science large scale missing data in a widely published reddit corpus"
    - "on the origins of memes by means of fringe web communities"
    - "conversation modeling on reddit using a graph structured lstm"
    - "community identity and user engagement in a multi community landscape"

Connected through hyperedge HE-4 (induced by source paper "sampling the news producers a large news and feature data set for the study of the complex media landscape"):
  with members:
    - "liar liar pants on fire a new benchmark dataset for fake news detection"
    - "acquiring background knowledge to improve moral value prediction"
    - "this just in fake news packs a lot in title uses simpler repetitive content in text body more similar to satire than real news"
    - "a stylometric inquiry into hyperpartisan and fake news"
    - "automatically identifying fake news in popular twitter threads"
    - "exploring the ideological nature of journalists social networks on twitter and associations with news story content"
    - "hoaxy a platform for tracking online misinformation"
    - "stop clickbait detecting and preventing clickbaits in online news media"

Connected through hyperedge HE-5 (induced by source paper "models for predicting community specific interest in news articles"):
  with members:
    - "sampling the news producers a large news and feature data set for the study of the complex media landscape"
    - "cats and captions vs creators and the clock comparing multimodal content to context in predicting relative popularity"
    - "the impact of crowds on news engagement a reddit case study"
    - "characterizing the language of online communities and its relation to community reception"
    - "breaking the news first impressions matter on online news"
    - "leveraging joint interactions for credibility analysis in news communities"
    - "talking to the crowd what do people react to in online discussions"
    - "a bayesian approach for predicting the popularity of tweets"

Connected through hyperedge HE-6 (induced by source paper "she s reddit a source of statistically significant gendered interest information"):
  with members:
    - "latent structure in collaboration the case of reddit r place"
    - "caveat emptor computational social science large scale missing data in a widely published reddit corpus"
    - "consumers and curators browsing and voting patterns on reddit"
    - "surviving an eternal september how an online community managed a surge of newcomers"
    - "gender based violence in 140 characters or fewer a bigdata case study of twitter"

Connected through hyperedge HE-7 (induced by source paper "the impact of crowds on news engagement a reddit case study"):
  with members:
    - "cats and captions vs creators and the clock comparing multimodal content to context in predicting relative popularity"
    - "science askscience and badscience on the coexistence of highly related communities"
    - "characterizing the language of online communities and its relation to community reception"
    - "breaking the news first impressions matter on online news"
    - "all who wander on the prevalence and characteristics of multi community engagement"
    - "talking to the crowd what do people react to in online discussions"
    - "a bayesian approach for predicting the popularity of tweets"
    - "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)