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: "dynamical classes of collective attention in twitter"
  Abstract: Micro-blogging systems such as Twitter expose digital traces of social discourse with an unprecedented degree of resolution of individual behaviors. They offer an opportunity to investigate how a large-scale social system responds to exogenous or endogenous stimuli, and to disentangle the temporal, spatial and topical aspects of users' activity. Here we focus on spikes of collective attention in Twitter, and specifically on peaks in the popularity of hashtags. Users employ hashtags as a form of social annotation, to define a shared context for a specific event, topic, or meme. We analyze a large-scale record of Twitter activity and find that the evolution of hastag popularity over time defines discrete classes of hashtags. We link these dynamical classes to the events the hashtags represent and use text mining techniques to provide a semantic characterization of the hastag classes. Moreover, we track the propagation of hashtags in the Twitter social network and find that epidemic spreading plays a minor role in hastag popularity, which is mostly driven by exogenous factors.

Connected through hyperedge HE-1 (induced by source paper "language in our time an empirical analysis of hashtags"):
  with members:
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "tagvisor a privacy advisor for sharing hashtags"
    - "hierarchical graph representation learning with differentiable pooling"
    - "you are your metadata identification and obfuscation of social media users using metadata information"
    - "analyzing the digital traces of political manipulation the 2016 russian interference twitter campaign"
    - "ml leaks model and data independent membership inference attacks and defenses on machine learning models"
    - "twitter for sparking a movement reddit for sharing the moment metoo through the lens of social media"
    - "is saki delicious the food perception gap on instagram and its relation to health"

Connected through hyperedge HE-2 (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"
    - "what trends in chinese social media"
    - "agents bookmarks and clicks a topical model of web navigation"

Connected through hyperedge HE-3 (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"
    - "information contagion an empirical study of the spread of news on digg and twitter social networks"
    - "novelty and collective attention"

Connected through hyperedge HE-4 (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"
    - "novelty and collective attention"

Connected through hyperedge HE-5 (induced by source paper "toward maximizing the visibility of content in social media brand pages a temporal analysis"):
  with members:
    - "online popularity under promotion viral potential forecasting and the economics of time"
    - "multistage campaigning in social networks"
    - "when to post on social networks"
    - "klout score measuring influence across multiple social networks"
    - "trends in social media persistence and decay"
    - "information contagion an empirical study of the spread of news on digg and twitter social networks"
    - "predicting the popularity of online content"

Connected through hyperedge HE-6 (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"
    - "novelty and collective attention"

Connected through hyperedge HE-7 (induced by source paper "anatomy of an online misinformation network"):
  with members:
    - "the spread of low credibility content by social bots"
    - "how algorithmic popularity bias hinders or promotes quality"
    - "disinformation and social bot operations in the run up to the 2017 french presidential election"
    - "early detection of promoted campaigns on social media"
    - "limited individual attention and online virality of low quality information"
    - "online human bot interactions detection estimation and characterization"
    - "hoaxy a platform for tracking online misinformation"
    - "botornot a system to evaluate social bots"

Connected through hyperedge HE-8 (induced by source paper "comparative studies of detecting abusive language on twitter"):
  with members:
    - "large scale crowdsourcing and characterization of twitter abusive behavior"
    - "mean birds detecting aggression and bullying on twitter"
    - "deep learning for hate speech detection in tweets"
    - "learning to rank question answer pairs using hierarchical recurrent encoder with latent topic clustering"
    - "modeling rich contexts for sentiment classification with lstm"
    - "adam a method for stochastic optimization"
    - "distributed representations of sentences and documents"
    - "convolutional neural networks for sentence classification"

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)