human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "tideh time dependent hawkes process for predicting retweet dynamics":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "socialgrid a tcn enhanced method for online discussion forecasting")
  Member "feature driven and point process approaches for popularity prediction":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "nestpp modeling thread dynamics in online discussion forums")
  Member "do cascades recur":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "estimating attention flow in online video networks")
  Member "exploring limits to prediction in complex social systems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "popularity prediction on social platforms with coupled graph neural networks")
  Member "modeling and predicting popularity dynamics of microblogs using self excited hawkes processes":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "socialgrid a tcn enhanced method for online discussion forecasting")
  Member "learning and forecasting opinion dynamics in social networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "consequential ranking algorithms and long term welfare")
  Member "seismic a self exciting point process model for predicting tweet popularity":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "inf vae a variational autoencoder framework to integrate homophily and influence in diffusion prediction")
  Member "shaping social activity by incentivizing users":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "self attentive hawkes processes")

Question: Please tell me which class the source paper 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)