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 "a comparison of methods for cascade prediction"):
  Abstract: Information cascades exist in a wide variety of platforms on Internet. A very important real-world problem is to identify which information cascades can go viral. A system addressing this problem can be used in a variety of applications including public health, marketing and counter-terrorism. As a cascade can be considered as compound of the social network and the time series. However, in related literature where methods for solving the cascade prediction problem were proposed, the experimental settings were often limited to only a single metric for a specific problem formulation. Moreover, little attention was paid to the run time of those methods. In this paper, we first formulate the cascade prediction problem as both classification and regression. Then we compare three categories of cascade prediction methods: centrality based, feature based and point process based. We carry out the comparison through evaluation of the methods by both accuracy metrics and run time. The results show that feature based methods can outperform others in terms of prediction accuracy but suffer from heavy overhead especially for large datasets. While point process based methods can also run into issue of long run time when the model can not well adapt to the data. This paper seeks to address issues in order to allow developers of systems for social network analysis to select the most appropriate method for predicting viral information cascades.

Includes members:
  Member "toward order of magnitude cascade prediction":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "understanding and forecasting lifecycle events in information cascades")
  Member "from micro to macro uncovering and predicting information cascading process with behavioral dynamics":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "popularity prediction on social platforms with coupled graph neural networks")
  Member "seismic a self exciting point process model for predicting tweet popularity":
    also connected through other hyperedges:
      - Hyperedge HE-3 (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-4 (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-5 (induced by source paper "deep collaborative embedding for information cascade prediction")
  Member "predicting successful memes using network and community structure":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a feature driven approach for identifying pathogenic social media accounts")
  Member "learning user specific latent influence and susceptibility from information cascades":
    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 "a data based approach to social influence maximization":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "use of a controlled experiment and computational models to measure the impact of sequential peer exposures on decision making")

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)