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: "novelty and collective attention"
  Abstract: The subject of collective attention is central to an information age where millions of people are inundated with daily messages. It is thus of interest to understand how attention to novel items propagates and eventually fades among large populations. We have analyzed the dynamics of collective attention among 1 million users of an interactive web site, digg.com, devoted to thousands of novel news stories. The observations can be described by a dynamical model characterized by a single novelty factor. Our measurements indicate that novelty within groups decays with a stretched-exponential law, suggesting the existence of a natural time scale over which attention fades.

Connected through hyperedge HE-1 (induced by source paper "modeling the popularity of twitter hashtags with master equations"):
  with members:
    - "influence of fake news in twitter during the 2016 us presidential election"
    - "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"

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 "a model for meme popularity growth in social networking systems based on biological principle and human interest dynamics"):
  with members:
    - "limited individual attention and online virality of low quality information"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "diffusion on dynamic contact networks with indirect transmission links"):
  with members:
    - "lightweight mobile ad hoc network routing protocols for smartphones"
    - "impact of indirect contacts in emerging infectious disease on social networks"
    - "least cost influence maximization across multiple social networks"
    - "diffusion in colocation contact networks the impact of nodal spatiotemporal dynamics"
    - "an introduction to temporal graphs an algorithmic perspective"
    - "common neighbours and the local community paradigm for topological link prediction in bipartite networks"
    - "latent self exciting point process model for spatial temporal networks"
    - "time varying graphs and dynamic networks"

Connected through hyperedge HE-5 (induced by source paper "discovering patterns of online popularity from time series"):
  with members:
    - "expecting to be hip hawkes intensity processes for social media popularity"
    - "toeplitz inverse covariance based clustering of multivariate time series data"
    - "online human bot interactions detection estimation and characterization"
    - "botornot a system to evaluate social bots"
    - "on the dynamics of social media popularity a youtube case study"
    - "is the sample good enough comparing data from twitter s streaming api with twitter s firehose"
    - "dynamical classes of collective attention in twitter"
    - "information contagion an empirical study of the spread of news on digg and twitter social networks"

Connected through hyperedge HE-6 (induced by source paper "characterizing collective attention via descriptor context a case study of public discussions of crisis events"):
  with members:
    - "pomo generating entity specific post modifiers in context"
    - "massively multilingual transfer for ner"
    - "the effect of collective attention on controversial debates on social media"
    - "credibility and dynamics of collective attention"
    - "globally normalized transition based neural networks"
    - "rising tides or rising stars dynamics of shared attention on twitter during media events"
    - "processing social media messages in mass emergency a survey"
    - "dynamical classes of collective attention in twitter"

Connected through hyperedge HE-7 (induced by source paper "interactional and informational attention on twitter"):
  with members:
    - "local variation of hashtag spike trains and popularity in twitter"
    - "when is it biased assessing the representativeness of twitter s streaming api"
    - "rising tides or rising stars dynamics of shared attention on twitter during media events"
    - "the bursty dynamics of the twitter information network"
    - "virality prediction and community structure in social networks"
    - "dynamical classes of collective attention in twitter"

Connected through hyperedge HE-8 (induced by source paper "wi fi sensing applications and challenges"):
  with members:
    - "privacy aware smart metering progress and challenges"
    - "low latency heterogeneous networks with millimeter wave communications"
    - "tensorbeat tensor decomposition for monitoring multi person breathing beats with commodity wifi"
    - "csi based fingerprinting for indoor localization a deep learning approach"
    - "monitoring breathing via signal strength in wireless networks"
    - "through wall tracking using variance based radio tomography networks"

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)