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: "i wanted to predict elections with twitter and all i got was this lousy paper a balanced survey on election prediction using twitter data"
  Abstract: Predicting X from Twitter is a popular fad within the Twitter research subculture. It seems both appealing and relatively easy. Among such kind of studies, electoral prediction is maybe the most attractive, and at this moment there is a growing body of literature on such a topic. This is not only an interesting research problem but, above all, it is extremely difficult. However, most of the authors seem to be more interested in claiming positive results than in providing sound and reproducible methods. It is also especially worrisome that many recent papers seem to only acknowledge those studies supporting the idea of Twitter predicting elections, instead of conducting a balanced literature review showing both sides of the matter. After reading many of such papers I have decided to write such a survey myself. Hence, in this paper, every study relevant to the matter of electoral prediction using social media is commented. From this review it can be concluded that the predictive power of Twitter regarding elections has been greatly exaggerated, and that hard research problems still lie ahead.

Connected through hyperedge HE-1 (induced by source paper "a total error framework for digital traces of humans"):
  with members:
    - "model cards for model reporting"
    - "demographic inference and representative population estimates from multilingual social media data"
    - "can you trust the trend discovering simpson s paradoxes in social data"
    - "how constraints affect content the case of twitter s switch from 140 to 280 characters"
    - "datasheets for datasets"
    - "false information on web and social media a survey"
    - "systematically monitoring social media the case of the german federal election 2017"
    - "sampling from social networks with attributes"

Connected through hyperedge HE-2 (induced by source paper "perils and challenges of social media and election manipulation analysis the 2018 us midterms"):
  with members:
    - "influence of fake news in twitter during the 2016 us presidential election"
    - "senator we sell ads analysis of the 2016 russian facebook ads campaign"
    - "analyzing the digital traces of political manipulation the 2016 russian interference twitter campaign"
    - "deep neural networks for bot detection"
    - "who falls for online political manipulation"
    - "the spread of low credibility content by social bots"
    - "fake news detection on social media a data mining perspective"
    - "early detection of promoted campaigns on social media"

Connected through hyperedge HE-3 (induced by source paper "demographic inference and representative population estimates from multilingual social media data"):
  with members:
    - "on the convergence of adam and beyond"
    - "bleaching text abstract features for cross lingual gender prediction"
    - "co training for demographic classification using deep learning from label proportions"
    - "word translation without parallel data"
    - "densely connected convolutional networks"
    - "a character level decoder without explicit segmentation for neural machine translation"
    - "whom should we sense in social sensing analyzing which users work best for social media now casting"
    - "character aware neural language models"

Connected through hyperedge HE-4 (induced by source paper "nowcasting the stance of social media users in a sudden vote the case of the greek referendum"):
  with members:
    - "political discourse on social media echo chambers gatekeepers and the price of bipartisanship"
    - "s i o no qu e penses catalonian independence and linguistic identity on social media"
    - "predicting twitter user socioeconomic attributes with network and language information"
    - "stance classification of social media users in independence movements"
    - "semeval 2017 task 8 rumoureval determining rumour veracity and support for rumours"
    - "i have a feeling trump will win forecasting winners and losers from user predictions on twitter"
    - "140 characters to victory using twitter to predict the uk 2015 general election"
    - "line large scale information network embedding"

Connected through hyperedge HE-5 (induced by source paper "using social media to predict the future a systematic literature review"):
  with members:
    - "what the language you tweet says about your occupation"
    - "mining online social data for detecting social network mental disorders"
    - "sentiment aggregate functions for political opinion polling using microblog streams"
    - "using user generated online photos to estimate and monitor air pollution in major cities"
    - "beating the news with embers forecasting civil unrest using open source indicators"
    - "predicting crowd behavior with big public data"
    - "big questions for social media big data representativeness validity and other methodological pitfalls"
    - "geotagging one hundred million twitter accounts with total variation minimization"

Connected through hyperedge HE-6 (induced by source paper "integrating sentiment and social structure to determine preference alignments the irish marriage referendum"):
  with members:
    - "in the mood the dynamics of collective sentiments on twitter"
    - "sentiment cascades in the 15m movement"
    - "is the sample good enough comparing data from twitter s streaming api with twitter s firehose"
    - "twitter reciprocal reply networks exhibit assortativity with respect to happiness"
    - "twitter mood predicts the stock market"
    - "seeing stars exploiting class relationships for sentiment categorization with respect to rating scales"
    - "thumbs up or thumbs down semantic orientation applied to unsupervised classification of reviews"

Connected through hyperedge HE-7 (induced by source paper "tales of emotion and stock in china volatility causality and prediction"):
  with members:
    - "emoticon based ambivalent expression a hidden indicator for unusual behaviors in weibo"
    - "can online emotions predict the stock market in china"
    - "twitter mood predicts the stock market"

Connected through hyperedge HE-8 (induced by source paper "semantic web today from oil rigs to panama papers"):
  with members:
    - "lb2co a semantic ontology framework for b2c ecommerce transaction on the internet"

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)