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: "modeling and predicting popularity dynamics of microblogs using self excited hawkes processes"
  Abstract: The ability to model and predict the popularity dynamics of individual user generated items on online media has important implications in a wide range of areas. In this paper, we propose a probabilistic model using a Self-Excited Hawkes Process(SEHP) to characterize the process through which individual microblogs gain their popularity. This model explicitly captures the triggering effect of each forwarding, distinguishing itself from the reinforced Poisson process based model where all previous forwardings are simply aggregated as a single triggering effect. We validate the proposed model by applying it on Sina Weibo, the most popular microblogging network in China. Experimental results demonstrate that the SEHP model consistently outperforms the model based on reinforced Poisson process.

Connected through hyperedge HE-1 (induced by source paper "socialgrid a tcn enhanced method for online discussion forecasting"):
  with members:
    - "nestpp modeling thread dynamics in online discussion forums"
    - "modelling structure and predicting dynamics of discussion threads in online boards"
    - "personalized thread recommendation for mooc discussion forums"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "time series classification from scratch with deep neural networks a strong baseline"
    - "multi scale convolutional neural networks for time series classification"
    - "tideh time dependent hawkes process for predicting retweet dynamics"
    - "wavenet a generative model for raw audio"

Connected through hyperedge HE-2 (induced by source paper "sir hawkes linking epidemic models and hawkes processes to model diffusions in finite populations"):
  with members:
    - "expecting to be hip hawkes intensity processes for social media popularity"
    - "the neural hawkes process a neurally self modulating multivariate point process"
    - "steering opinion dynamics in information diffusion networks"
    - "a stochastic differential equation framework for guiding online user activities in closed loop"
    - "tideh time dependent hawkes process for predicting retweet dynamics"
    - "feature driven and point process approaches for popularity prediction"
    - "exploring limits to prediction in complex social systems"
    - "from micro to macro uncovering and predicting information cascading process with behavioral dynamics"

Connected through hyperedge HE-3 (induced by source paper "modeling and predicting popularity dynamics via deep learning attention mechanism"):
  with members:
    - "towards binary valued gates for robust lstm training"
    - "attention is all you need"
    - "modeling the intensity function of point process via recurrent neural networks"
    - "retain interpretable predictive model in healthcare using reverse time attention mechanism"
    - "feed forward networks with attention can solve some long term memory problems"
    - "describing multimedia content using attention based encoder decoder networks"
    - "sequence to sequence learning with neural networks"
    - "popularity prediction in microblogging network a case study on sina weibo"

Connected through hyperedge HE-4 (induced by source paper "weseer visual analysis for better information cascade prediction of wechat articles"):
  with members:
    - "the lifecycle and cascade of wechat social messaging groups"
    - "seismic a self exciting point process model for predicting tweet popularity"
    - "can cascades be predicted"
    - "popularity prediction in microblogging network a case study on sina weibo"
    - "cumulative effect in information diffusion empirical study on a microblogging network"
    - "the pulse of news in social media forecasting popularity"
    - "structure and dynamics of information pathways in online media"

Connected through hyperedge HE-5 (induced by source paper "expecting to be hip hawkes intensity processes for social media popularity"):
  with members:
    - "tideh time dependent hawkes process for predicting retweet dynamics"
    - "feature driven and point process approaches for popularity prediction"
    - "do cascades recur"
    - "exploring limits to prediction in complex social systems"
    - "learning and forecasting opinion dynamics in social networks"
    - "seismic a self exciting point process model for predicting tweet popularity"
    - "shaping social activity by incentivizing users"
    - "modeling and predicting popularity dynamics via reinforced poisson processes"

Connected through hyperedge HE-6 (induced by source paper "marked temporal dynamics modeling based on recurrent neural network"):
  with members:
    - "orthogonal rnns and long memory tasks"
    - "adam a method for stochastic optimization"
    - "modeling and predicting popularity dynamics via reinforced poisson processes"
    - "deepwalk online learning of social representations"
    - "modeling information propagation with survival theory"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"
    - "predicting the popularity of online content"

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)