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: "tideh time dependent hawkes process for predicting retweet dynamics"
  Abstract: Online social networking services allow their users to post content in the form of text, images or videos. The main mechanism driving content diffusion is the possibility for users to re-share the content posted by their social connections, which may then cascade across the system. A fundamental problem when studying information cascades is the possibility to develop sound mathematical models, whose parameters can be calibrated on empirical data, in order to predict the future course of a cascade after a window of observation. In this paper, we focus on Twitter and, in particular, on the temporal patterns of retweet activity for an original tweet. We model the system by Time-Dependent Hawkes process (TiDeH), which properly takes into account the circadian nature of the users and the aging of information. The input of the prediction model are observed retweet times and structural information about the underlying social network. We develop a procedure for parameter optimization and for predicting the future profiles of retweet activity at different time resolutions. We validate our methodology on a large corpus of Twitter data and demonstrate its systematic improvement over existing approaches in all the time regimes.

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"
    - "wavenet a generative model for raw audio"
    - "a convolutional encoder model for neural machine translation"

Connected through hyperedge HE-2 (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"
    - "expecting to be hip hawkes intensity processes for social media popularity"
    - "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-3 (induced by source paper "predictive analysis on twitter techniques and applications"):
  with members:
    - "what s my age predicting twitter user s age using influential friend network and dbpedia"
    - "multiview deep learning for predicting twitter users location"
    - "on the challenges of sentiment analysis for dynamic events"
    - "early detection of promoted campaigns on social media"
    - "emojinet an open service and api for emoji sense discovery"
    - "location name extraction from targeted text streams using gazetteer based statistical language models"
    - "using social media to predict the future a systematic literature review"
    - "semi supervised approach to monitoring clinical depressive symptoms in social media"

Connected through hyperedge HE-4 (induced by source paper "meta learning with relational information for short sequences"):
  with members:
    - "on first order meta learning algorithms"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "reptile a scalable metalearning algorithm"
    - "probabilistic model agnostic meta learning"
    - "motifs in temporal networks"
    - "prototypical networks for few shot learning"
    - "fake news mitigation via point process based intervention"
    - "a dirichlet mixture model of hawkes processes for event sequence clustering"

Connected through hyperedge HE-5 (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"
    - "expecting to be hip hawkes intensity processes for social media popularity"

Connected through hyperedge HE-6 (induced by source paper "modelling online comment threads from their start"):
  with members:
    - "modelling structure and predicting dynamics of discussion threads in online boards"
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "guessthekarma a game to assess social rating systems"
    - "rating effects on social news posts and comments"
    - "deep reinforcement learning with a combinatorial action space for predicting and tracking popular discussion threads"
    - "deep reinforcement learning with a combinatorial action space for predicting popular reddit threads"
    - "node2vec scalable feature learning for networks"
    - "seismic a self exciting point process model for predicting tweet popularity"

Connected through hyperedge HE-7 (induced by source paper "smp challenge an overview of social media prediction challenge 2019"):
  with members:
    - "sequential prediction of social media popularity with deep temporal context networks"
    - "time matters multi scale temporalization of social media popularity"
    - "yelp dataset challenge review rating prediction"
    - "visual genome connecting language and vision using crowdsourced dense image annotations"
    - "predictability of popularity gaps between prediction and understanding"
    - "exploring limits to prediction in complex social systems"
    - "seismic a self exciting point process model for predicting tweet popularity"
    - "online popularity and topical interests through the lens of instagram"

Connected through hyperedge HE-8 (induced by source paper "modelling structure and predicting dynamics of discussion threads in online boards"):
  with members:
    - "all who wander on the prevalence and characteristics of multi community engagement"
    - "local variation of hashtag spike trains and popularity in twitter"
    - "seismic a self exciting point process model for predicting tweet popularity"
    - "modeling and predicting popularity dynamics via reinforced poisson processes"
    - "can cascades be predicted"
    - "a likelihood based framework for the analysis of discussion threads"
    - "quantifying long term scientific impact"
    - "from user comments to on line conversations"

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)