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: "expecting to be hip hawkes intensity processes for social media popularity"
  Abstract: Modeling and predicting the popularity of online content is a significant problem for the practice of information dissemination, advertising, and consumption. Recent work analyzing massive datasets advances our understanding of popularity, but one major gap remains: To precisely quantify the relationship between the popularity of an online item and the external promotions it receives. This work supplies the missing link between exogenous inputs from public social media platforms, such as Twitter, and endogenous responses within the content platform, such as YouTube. We develop a novel mathematical model, the Hawkes intensity process, which can explain the complex popularity history of each video according to its type of content, network of diffusion, and sensitivity to promotion. Our model supplies a prototypical description of videos, called an endo-exo map. This map explains popularity as the result of an extrinsic factor -- the amount of promotions from the outside world that the video receives, acting upon two intrinsic factors -- sensitivity to promotion, and inherent virality. We use this model to forecast future popularity given promotions on a large 5-months feed of the most-tweeted videos, and found it to lower the average error by 28.6% from approaches based on popularity history. Finally, we can identify videos that have a high potential to become viral, as well as those for which promotions will have hardly any effect.

Connected through hyperedge HE-1 (induced by source paper "nestpp modeling thread dynamics in online discussion forums"):
  with members:
    - "modelling structure and predicting dynamics of discussion threads in online boards"
    - "personalized thread recommendation for mooc discussion forums"
    - "tideh time dependent hawkes process for predicting retweet dynamics"
    - "feature driven and point process approaches for popularity prediction"
    - "seismic a self exciting point process model for predicting tweet popularity"
    - "can cascades be predicted"
    - "from user comments to on line conversations"

Connected through hyperedge HE-2 (induced by source paper "long term trends of diversity online"):
  with members:
    - "feature driven and point process approaches for popularity prediction"

Connected through hyperedge HE-3 (induced by source paper "popularity prediction on social platforms with coupled graph neural networks"):
  with members:
    - "graph wavelet neural network"
    - "graph convolutional neural networks for web scale recommender systems"
    - "hierarchical graph representation learning with differentiable pooling"
    - "deepinf social influence prediction with deep learning"
    - "how powerful are graph neural networks"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "neural message passing for quantum chemistry"
    - "topological recurrent neural network for diffusion prediction"

Connected through hyperedge HE-4 (induced by source paper "neural jump stochastic differential equations"):
  with members:
    - "latent odes for irregularly sampled time series"
    - "diffeqflux jl a julia library for neural differential equations"
    - "neural stochastic differential equations deep latent gaussian models in the diffusion limit"
    - "a differentiable programming system to bridge machine learning and scientific computing"
    - "hidden physics models machine learning of nonlinear partial differential equations"
    - "solving for high dimensional committor functions using artificial neural networks"
    - "a comparison of automatic differentiation and continuous sensitivity analysis for derivatives of differential equation solutions"
    - "a dirichlet mixture model of hawkes processes for event sequence clustering"

Connected through hyperedge HE-5 (induced by source paper "estimating attention flow in online video networks"):
  with members:
    - "offline evaluation of ranking policies with click models"
    - "top k off policy correction for a reinforce recommender system"
    - "sir hawkes linking epidemic models and hawkes processes to model diffusions in finite populations"
    - "foundations of sequence to sequence modeling for time series"
    - "beyond views measuring and predicting engagement on youtube videos"
    - "online popularity under promotion viral potential forecasting and the economics of time"
    - "do cascades recur"
    - "exploring limits to prediction in complex social systems"

Connected through hyperedge HE-6 (induced by source paper "using network motifs to characterize temporal network evolution leading to diffusion inhibition"):
  with members:
    - "understanding and forecasting lifecycle events in information cascades"
    - "higher order organization of complex networks"
    - "do cascades recur"
    - "toward early and order of magnitude cascade prediction in social networks"
    - "coevolve a joint point process model for information diffusion and network co evolution"
    - "seismic a self exciting point process model for predicting tweet popularity"
    - "can cascades be predicted"
    - "large social networks can be targeted for viral marketing with small seed sets"

Connected through hyperedge HE-7 (induced by source paper "discovering patterns of online popularity from time series"):
  with members:
    - "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"
    - "novelty and collective attention"

Connected through hyperedge HE-8 (induced by source paper "modeling information cascades with self exciting processes via generalized epidemic models"):
  with members:
    - "arming the public with artificial intelligence to counter social bots"
    - "modeling popularity in asynchronous social media streams with recurrent neural networks"
    - "the neural hawkes process a neurally self modulating multivariate point process"
    - "feature driven and point process approaches for popularity prediction"
    - "exploring limits to prediction in complex social systems"
    - "seismic a self exciting point process model for predicting tweet popularity"
    - "uncovering the temporal dynamics of diffusion 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.SI(Social and Information Networks)