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 "modelling structure and predicting dynamics of discussion threads in online boards"):
  Abstract: Internet boards are platforms for online discussion about a variety of topics. On these boards, individuals may start a new thread on a specific matter or leave comments in an existing discussion. The resulting collective process leads to the formation of ‘discussion trees’, where nodes represent a post and comments, and an edge represents a ‘reply-to’ relation. The structure of discussion trees has been analysed in previous works, but mainly from a static perspective. In this article, we focus on the structural and dynamical properties of discussion trees by modelling their formation as a self-exciting Hawkes process with a branching structure. We first study a Reddit data set of all posts and comments submitted between 2008 and 2014 to show that the structure of the trees resemble those produced by a Galton–Watson process with a special root offspring distribution. The dynamical aspect of the model is then used to predict future commenting activity and the final size of a discussion tree. We compare the efficiency of our approach with previous works and show its superiority for the prediction of discussion dynamics. In particular, we find that the Reddit discussion trees, being fundamentally broader than for example the Twitter cascades and developing with a qualitatively different dynamics, require appropriate models. The tree structure and the dynamics are influenced mainly by the age of the discussion, rather than by the number of acquired comments. The post and the comments are further commented by different processes, which is a key ingredient in modelling of discussions.

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 "all who wander on the prevalence and characteristics of multi community engagement":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "content removal as a moderation strategy compliance and other outcomes in the changemyview community")
  Member "local variation of hashtag spike trains and popularity in twitter":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a model for meme popularity growth in social networking systems based on biological principle and human interest dynamics")
  Member "seismic a self exciting point process model for predicting tweet popularity":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "inf vae a variational autoencoder framework to integrate homophily and influence in diffusion prediction")
  Member "modeling and predicting popularity dynamics via reinforced poisson processes":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deepcp deep learning driven cascade prediction based autonomous content placement in closed social network")
  Member "can cascades be predicted":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep collaborative embedding for information cascade prediction")
  Member "a likelihood based framework for the analysis of discussion threads":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "the anatomy of reddit an overview of academic research")
  Member "quantifying long term scientific impact":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "how well does i3 perform for impact measurement compared to other bibliometric indicators the convergent validity of several field normalized indicators")

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)