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: "controversy and sentiment in online news"
  Abstract: How do news sources tackle controversial issues? In this work, we take a data-driven approach to understand how controversy interplays with emotional expression and biased language in the news. We begin by introducing a new dataset of controversial and noncontroversial terms collected using crowdsourcing. Then, focusing on 15 major U.S. news outlets, we compare millions of articles discussing controversial and non-controversial issues over a span of 7 months. We find that in general, when it comes to controversial issues, the use of negative affect and biased language is prevalent, while the use of strong emotion is tempered. We also observe many differences across news sources. Using these findings, we show that we can indicate to what extent an issue is controversial, by comparing it with other issues in terms of how they are portrayed across different media.

Connected through hyperedge HE-1 (induced by source paper "characterizing behavioral trends in a community driven discussion platform"):
  with members:
    - "analyzing behavioral trends in community driven discussion platforms like reddit"
    - "caveat emptor computational social science large scale missing data in a widely published reddit corpus"
    - "consumers and curators browsing and voting patterns on reddit"
    - "predicting user interactions on reddit"
    - "rating effects on social news posts and comments"
    - "popularity and quality in social news aggregators a study of reddit and hacker news"
    - "evolution of reddit from the front page of the internet to a self referential community"

Connected through hyperedge HE-2 (induced by source paper "finding large balanced subgraphs in signed networks"):
  with members:
    - "discovering polarized communities in signed networks"
    - "reducing controversy by connecting opposing views"
    - "a survey of signed network mining in social media"
    - "maximum balanced subgraph problem parameterized above lower bound"

Connected through hyperedge HE-3 (induced by source paper "something s brewing early prediction of controversy causing posts from discussion features"):
  with members:
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "winning arguments interaction dynamics and persuasion strategies in good faith online discussions"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "adam a method for stochastic optimization"
    - "characterizing and curating conversation threads expansion focus volume re entry"
    - "distributed representations of words and phrases and their compositionality"
    - "predicting the popularity of online content"

Connected through hyperedge HE-4 (induced by source paper "discovering polarized communities in signed networks"):
  with members:
    - "reducing controversy by connecting opposing views"
    - "signed link analysis in social media networks"
    - "a survey of signed network mining in social media"
    - "correlation clustering with constrained cluster sizes and extended weights bounds"
    - "influence diffusion dynamics and influence maximization in social networks with friend and foe relationships"
    - "the slashdot zoo mining a social network with negative edges"
    - "correlation clustering with a fixed number of clusters"

Connected through hyperedge HE-5 (induced by source paper "on the complexity of opinions and online discussions"):
  with members:
    - "deep sentence embedding using long short term memory networks analysis and application to information retrieval"
    - "learning and forecasting opinion dynamics in social networks"
    - "the complexity of computing the minimum rank of a sign pattern matrix"
    - "distributed representations of sentences and documents"
    - "convolutional neural networks for sentence classification"
    - "an approximation algorithm for approximation rank"

Connected through hyperedge HE-6 (induced by source paper "community interaction and conflict on the web"):
  with members:
    - "false information on web and social media a survey"
    - "community identity and user engagement in a multi community landscape"
    - "an army of me sockpuppets in online discussion communities"
    - "contagion dynamics of extremist propaganda in social networks"
    - "representation learning on graphs methods and applications"
    - "anyone can become a troll causes of trolling behavior in online discussions"
    - "everything i disagree with is fakenews correlating political polarization and spread of misinformation"
    - "loyalty in online communities"

Connected through hyperedge HE-7 (induced by source paper "polarization and fake news early warning of potential misinformation targets"):
  with members:
    - "polarization of the vaccination debate on facebook"
    - "misinformation spreading on facebook"
    - "it s always april fools day on the difficulty of social network misinformation classification via propagation features"
    - "leveraging the crowd to detect and reduce the spread of fake news and misinformation"
    - "fake news detection on social media a data mining perspective"
    - "automatically identifying fake news in popular twitter threads"
    - "tweet acts a speech act classifier for twitter"
    - "debunking in a world of tribes"

Connected through hyperedge HE-8 (induced by source paper "measuring understanding and classifying news media sympathy on twitter after crisis events"):
  with members:
    - "are you charlie or ahmed cultural pluralism in charlie hebdo response on twitter"
    - "a primer on neural network models for natural language processing"
    - "convolutional neural networks for sentence classification"
    - "content and network dynamics behind egyptian political polarization on twitter"
    - "understanding news geography and major determinants of global news coverage of disasters"
    - "efficient estimation of word representations in vector space"
    - "adadelta an adaptive learning rate method"
    - "good friends bad news affect and virality in twitter"

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.CY(Computers and Society)