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: "seismic a self exciting point process model for predicting tweet popularity"
  Abstract: Social networking websites allow users to create and share content. Big information cascades of post resharing can form as users of these sites reshare others' posts with their friends and followers. One of the central challenges in understanding such cascading behaviors is in forecasting information outbreaks, where a single post becomes widely popular by being reshared by many users. In this paper, we focus on predicting the final number of reshares of a given post. We build on the theory of self-exciting point processes to develop a statistical model that allows us to make accurate predictions. Our model requires no training or expensive feature engineering. It results in a simple and efficiently computable formula that allows us to answer questions, in real-time, such as: Given a post's resharing history so far, what is our current estimate of its final number of reshares? Is the post resharing cascade past the initial stage of explosive growth? And, which posts will be the most reshared in the future? We validate our model using one month of complete Twitter data and demonstrate a strong improvement in predictive accuracy over existing approaches. Our model gives only 15% relative error in predicting final size of an average information cascade after observing it for just one hour.

Connected through hyperedge HE-1 (induced by source paper "inf vae a variational autoencoder framework to integrate homophily and influence in diffusion prediction"):
  with members:
    - "an induced multi relational framework for answer selection in community question answer platforms"
    - "deepinf social influence prediction with deep learning"
    - "dynamic graph representation learning via self attention networks"
    - "attention is all you need"
    - "representation learning on graphs methods and applications"
    - "motif based convolutional neural network on graphs"
    - "topological recurrent neural network for diffusion prediction"
    - "node2vec scalable feature learning for networks"

Connected through hyperedge HE-2 (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-3 (induced by source paper "learning multivariate hawkes processes at scale"):
  with members:
    - "predicting dynamic embedding trajectory in temporal interaction networks"
    - "latent odes for irregularly sampled time series"
    - "community interaction and conflict on the web"
    - "sir hawkes linking epidemic models and hawkes processes to model diffusions in finite populations"
    - "fake news mitigation via point process based intervention"
    - "wasserstein learning of deep generative point process models"
    - "learning from clinical judgments semi markov modulated marked hawkes processes for risk prognosis"
    - "learning granger causality for hawkes processes"

Connected through hyperedge HE-4 (induced by source paper "deepcp deep learning driven cascade prediction based autonomous content placement in closed social network"):
  with members:
    - "edge intelligence paving the last mile of artificial intelligence with edge computing"
    - "edge ai on demand accelerating deep neural network inference via edge computing"
    - "improved techniques for training gans"
    - "social network analysis inspired content placement with qos in cloud based content delivery networks"
    - "adam a method for stochastic optimization"
    - "modeling and predicting popularity dynamics via reinforced poisson processes"
    - "can cascades be predicted"
    - "living on the edge the role of proactive caching in 5g wireless networks"

Connected through hyperedge HE-5 (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"
    - "tideh time dependent hawkes process for predicting retweet dynamics"
    - "feature driven and point process approaches for popularity prediction"
    - "can cascades be predicted"
    - "from user comments to on line conversations"

Connected through hyperedge HE-6 (induced by source paper "address event variable length compression for time encoded data"):
  with members:
    - "self attentive hawkes processes"
    - "learning granger causality for hawkes processes"
    - "covering point patterns"

Connected through hyperedge HE-7 (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"
    - "expecting to be hip hawkes intensity processes for social media popularity"
    - "neural message passing for quantum chemistry"

Connected through hyperedge HE-8 (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"

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)