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: "limited individual attention and online virality of low quality information"
  Abstract: Social media are massive marketplaces where ideas and news compete for our attention. Previous studies have shown that quality is not a necessary condition for online virality and that knowledge about peer choices can distort the relationship between quality and popularity. However, these results do not explain the viral spread of low-quality information, such as the digital misinformation that threatens our democracy. We investigate quality discrimination in a stylized model of online social network, where individual agents prefer quality information, but have behavioral limitations in managing a heavy flow of information. We measure the relationship between the quality of an idea and its likelihood to become prevalent at the system level. We find that both information overload and limited attention contribute to a degradation in the market's discriminative power. A good tradeoff between discriminative power and diversity of information is possible according to the model. However, calibration with empirical data characterizing information load and finite attention in real social media reveals a weak correlation between quality and popularity of information. In these realistic conditions, the model predicts that high-quality information has little advantage over low-quality information.

Connected through hyperedge HE-1 (induced by source paper "mining social media data for biomedical signals and health related behavior"):
  with members:
    - "singular value decomposition and neural networks"
    - "anatomy of an online misinformation network"
    - "human sexual cycles are driven by culture and match collective moods"
    - "quantified self meets social media sharing of weight updates on twitter"
    - "the rise of social bots"
    - "learning deep structure preserving image text embeddings"
    - "insights from machine learned diet success prediction"
    - "benchmarking sentiment analysis methods for large scale texts a case for using continuum scored words and word shift graphs"

Connected through hyperedge HE-2 (induced by source paper "information pollution by social bots"):
  with members:
    - "arming the public with artificial intelligence to counter social bots"
    - "the role of bot squads in the political propaganda on twitter"
    - "identifying and analyzing cryptocurrency manipulations in social media"
    - "analyzing the digital traces of political manipulation the 2016 russian interference twitter campaign"
    - "deep neural networks for bot detection"
    - "quantifying biases in online information exposure"
    - "the spread of low credibility content by social bots"
    - "how algorithmic popularity bias hinders or promotes quality"

Connected through hyperedge HE-3 (induced by source paper "influence of fake news in twitter during the 2016 us presidential election"):
  with members:
    - "anatomy of an online misinformation network"
    - "the spread of low credibility content by social bots"
    - "validation of twitter opinion trends with national polling aggregates hillary clinton vs donald trump"
    - "online human bot interactions detection estimation and characterization"
    - "users polarization on facebook and youtube"
    - "hoaxy a platform for tracking online misinformation"
    - "the anatomy of brexit debate on facebook"
    - "the rise of social bots"

Connected through hyperedge HE-4 (induced by source paper "a model for meme popularity growth in social networking systems based on biological principle and human interest dynamics"):
  with members:
    - "local variation of hashtag spike trains and popularity in twitter"
    - "seismic a self exciting point process model for predicting tweet popularity"
    - "inheritance patterns in citation networks reveal scientific memes"
    - "rising tides or rising stars dynamics of shared attention on twitter during media events"
    - "a bayesian approach for predicting the popularity of tweets"
    - "dynamical classes of collective attention in twitter"
    - "what trends in chinese social media"
    - "agents bookmarks and clicks a topical model of web navigation"

Connected through hyperedge HE-5 (induced by source paper "on the inevitability of online echo chambers"):
  with members:
    - "quantifying biases in online information exposure"
    - "how algorithmic popularity bias hinders or promotes quality"
    - "explaining the emergence of echo chambers on social media the role of ideology and extremism"
    - "measuring online social bubbles"
    - "the role of information diffusion in the evolution of social networks"
    - "partisan asymmetries in online political activity"
    - "happiness is assortative in online social networks"
    - "topical interests and the mitigation of search engine bias"

Connected through hyperedge HE-6 (induced by source paper "the complexity of social media response statistical evidence for one dimensional engagement signal in twitter"):
  with members:
    - "scalable privacy compliant virality prediction on twitter"
    - "quote rts on twitter usage of the new feature for political discourse"
    - "good friends bad news affect and virality in twitter"

Connected through hyperedge HE-7 (induced by source paper "deception strategies and threats for online discussions"):
  with members:
    - "influence of fake news in twitter during the 2016 us presidential election"
    - "anatomy of an online misinformation network"
    - "the spread of low credibility content by social bots"
    - "soc2seq social embedding meets conversation model"
    - "reuters tracer toward automated news production using large scale social media data"
    - "early detection of promoted campaigns on social media"
    - "discovery retrieval and analysis of star wars botnet in twitter"
    - "the fake news spreading plague was it preventable"

Connected through hyperedge HE-8 (induced by source paper "the spread of low credibility content by social bots"):
  with members:
    - "anatomy of an online misinformation network"
    - "detection and resolution of rumours in social media a survey"
    - "quantifying biases in online information exposure"
    - "how algorithmic popularity bias hinders or promotes quality"
    - "fake news detection on social media a data mining perspective"
    - "disinformation and social bot operations in the run up to the 2017 french presidential election"
    - "early detection of promoted campaigns on social media"
    - "online human bot interactions detection estimation and characterization"

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)