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: "influence of fake news in twitter during the 2016 us presidential election"
  Abstract: The dynamics and influence of fake news on Twitter during the 2016 US presidential election remains to be clarified. Here, we use a dataset of 171 million tweets in the five months preceding the election day to identify 30 million tweets, from 2.2 million users, which contain a link to news outlets. Based on a classification of news outlets curated by #N#                  www.opensources.co#N#                  #N#                , we find that 25% of these tweets spread either fake or extremely biased news. We characterize the networks of information flow to find the most influential spreaders of fake and traditional news and use causal modeling to uncover how fake news influenced the presidential election. We find that, while top influencers spreading traditional center and left leaning news largely influence the activity of Clinton supporters, this causality is reversed for the fake news: the activity of Trump supporters influences the dynamics of the top fake news spreaders.

Connected through hyperedge HE-1 (induced by source paper "topology comparison of twitter diffusion networks effectively reveals misleading information"):
  with members:
    - "false news on social media a data driven survey"
    - "fake news detection on social media using geometric deep learning"
    - "an information theoretic all scales approach to comparing networks"
    - "anatomy of an online misinformation network"
    - "analyzing the digital traces of political manipulation the 2016 russian interference twitter campaign"
    - "the spread of low credibility content by social bots"
    - "validation of twitter opinion trends with national polling aggregates hillary clinton vs donald trump"
    - "feature selection a data perspective"

Connected through hyperedge HE-2 (induced by source paper "uncovering coordinated networks on social media"):
  with members:
    - "identifying and analyzing cryptocurrency manipulations in social media"
    - "bot electioneering volume visualizing social bot activity during elections"
    - "an unsupervised approach to detect spam campaigns that use botnets on twitter"
    - "the spread of low credibility content by social bots"
    - "disinformation and social bot operations in the run up to the 2017 french presidential election"
    - "the paradigm shift of social spambots evidence theories and tools for the arms race"
    - "online human bot interactions detection estimation and characterization"
    - "botornot a system to evaluate social bots"

Connected through hyperedge HE-3 (induced by source paper "a multi layer approach to disinformation detection on twitter"):
  with members:
    - "investigating italian disinformation spreading on twitter in the context of 2019 european elections"
    - "fake news detection on social media using geometric deep learning"
    - "defending against neural fake news"
    - "anatomy of an online misinformation network"
    - "polarization and fake news early warning of potential misinformation targets"
    - "analyzing the digital traces of political manipulation the 2016 russian interference twitter campaign"
    - "the spread of low credibility content by social bots"
    - "hoaxy a platform for tracking online misinformation"

Connected through hyperedge HE-4 (induced by source paper "unbiased evaluation of ranking metrics reveals consistent performance in science and technology citation data"):
  with members:
    - "early identification of important patents design and validation of citation network metrics"
    - "bias amplification in artificial intelligence systems"
    - "a review of theory and practice in scientometrics"
    - "a review of the literature on citation impact indicators"
    - "methods for the generation of normalized citation impact scores in bibliometrics which method best reflects the judgements of experts"
    - "coauthorship and citation patterns in the physical review"
    - "the use of percentiles and percentile rank classes in the analysis of bibliometric data opportunities and limits"
    - "quantifying the influence of scientists and their publications distinguishing between prestige and popularity"

Connected through hyperedge HE-5 (induced by source paper "the covid 19 social media infodemic"):
  with members:
    - "polarization and fake news early warning of potential misinformation targets"
    - "polarization of the vaccination debate on facebook"
    - "quantifying search bias investigating sources of bias for political searches in social media"
    - "modeling the infectiousness of twitter hashtags"
    - "debunking in a world of tribes"
    - "science vs conspiracy collective narratives in the age of misinformation"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-6 (induced by source paper "modeling the popularity of twitter hashtags with master equations"):
  with members:
    - "validation of twitter opinion trends with national polling aggregates hillary clinton vs donald trump"
    - "modeling the infectiousness of twitter hashtags"
    - "virality prediction and community structure in social networks"
    - "the pulse of news in social media forecasting popularity"
    - "stochastic models of user contributory web sites"
    - "novelty and collective attention"

Connected through hyperedge HE-7 (induced by source paper "traffic networks are vulnerable to disinformation attacks"):
  with members:
    - "the spread of low credibility content by social bots"
    - "disinformation and social bot operations in the run up to the 2017 french presidential election"

Connected through hyperedge HE-8 (induced by source paper "the mass fake news and cognition security"):
  with members:
    - "the role of user profile for fake news detection"
    - "studying fake news via network analysis detection and mitigation"
    - "techniques for interpretable machine learning"
    - "declare debunking fake news and false claims using evidence aware deep learning"
    - "fake news a survey of research detection methods and opportunities"
    - "opinion dynamics with varying susceptibility to persuasion"
    - "deepinf social influence prediction with deep learning"
    - "detection and resolution of rumours in social media a survey"

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)