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: "a meta analysis of state of the art electoral prediction from twitter data"
  Abstract: Electoral prediction from Twitter data is an appealing research topic. It seems relatively straightforward and the prevailing view is overly optimistic. This is problematic because while simple approaches are assumed to be good enough, core problems are not addressed. Thus, this article aims to (1) provide a balanced and critical review of the state of the art; (2) cast light on the presume predictive power of Twitter data; and (3) propose some considerations to push forward the field. Hence, a scheme to characterize Twitter prediction methods is proposed. It covers every aspect from data collection to performance evaluation, through data processing and vote inference. Using that scheme, prior research is analyzed and organized to explain the main approaches taken up to date but also their weaknesses. This is the first meta-analysis of the whole body of research regarding electoral prediction from Twitter data. It reveals that its presumed predictive power regarding electoral prediction has been somewhat exaggerated: Social media may provide a glimpse on electoral outcomes but, up to now, research has not provided strong evidence to support it can currently replace traditional polls. Nevertheless, there are some reasons for optimism and, hence, further work on this topic is required, along with tighter integration with traditional electoral forecasting research.

Connected through hyperedge HE-1 (induced by source paper "measuring relative opinion from location based social media a case study of the 2016 u s presidential election"):
  with members:
    - "validation of twitter opinion trends with national polling aggregates hillary clinton vs donald trump"
    - "election bias comparing polls and twitter in the 2016 u s election"
    - "scaling laws in geo located twitter data"
    - "using word embeddings in twitter election classification"
    - "distributed representations of sentences and documents"
    - "deep learning of representations looking forward"
    - "distributed representations of words and phrases and their compositionality"
    - "natural language processing almost from scratch"

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 "validation of twitter opinion trends with national polling aggregates hillary clinton vs donald trump"):
  with members:
    - "online human bot interactions detection estimation and characterization"
    - "deciphering the 2016 u s presidential campaign in the twitter sphere a comparison of the trumpists and clintonists"
    - "the effects of twitter sentiment on stock price returns"
    - "content and network dynamics behind egyptian political polarization on twitter"
    - "is the sample good enough comparing data from twitter s streaming api with twitter s firehose"
    - "partisan asymmetries in online political activity"
    - "twitter mood predicts the stock market"

Connected through hyperedge HE-5 (induced by source paper "a user modeling pipeline for studying polarized political events in social media"):
  with members:
    - "disinformation and social bot operations in the run up to the 2017 french presidential election"
    - "online human bot interactions detection estimation and characterization"
    - "botornot a system to evaluate social bots"
    - "distributed representations of words and phrases and their compositionality"
    - "truthy mapping the spread of astroturf in microblog streams"
    - "influence and passivity in social media"

Connected through hyperedge HE-6 (induced by source paper "does campaigning on social media make a difference evidence from candidate use of twitter during the 2015 and 2017 uk elections"):
  with members:
    - "wikipedia traffic data and electoral prediction towards theoretically informed models"
    - "where in the world are you geolocation and language identification in twitter"

Connected through hyperedge HE-7 (induced by source paper "on the influence of social bots in online protests"):
  with members:
    - "botornot a system to evaluate social bots"
    - "the rise of social bots"
    - "bots strongerin and brexit computational propaganda during the uk eu referendum"
    - "sifting robotic from organic text a natural language approach for detecting automation on twitter"
    - "vaporous marketing uncovering pervasive electronic cigarette advertisements on twitter"
    - "the new war correspondents the rise of civic media curation in urban warfare"
    - "information transfer in social media"

Connected through hyperedge HE-8 (induced by source paper "predicting election trends with twitter hillary clinton versus donald trump"):
  with members:
    - "deciphering the 2016 u s presidential campaign in the twitter sphere a comparison of the trumpists and clintonists"
    - "content and network dynamics behind egyptian political polarization on twitter"
    - "is the sample good enough comparing data from twitter s streaming api with twitter s firehose"
    - "partisan asymmetries in online political activity"
    - "twitter mood predicts the stock market"

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)