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 "modeling popularity in asynchronous social media streams with recurrent neural networks"):
  Abstract: Understanding and predicting the popularity of online items is an important open problem in social media analysis. Considerable progress has been made recently in data-driven predictions, and in linking popularity to external promotions. However, the existing methods typically focus on a single source of external influence, whereas for many types of online content such as YouTube videos or news articles, attention is driven by multiple heterogeneous sources simultaneously - e.g. microblogs or traditional media coverage. Here, we propose RNN-MAS, a recurrent neural network for modeling asynchronous streams. It is a sequence generator that connects multiple streams of different granularity via joint inference. We show RNN-MAS not only to outperform the current state-of-the-art Youtube popularity prediction system by 17%, but also to capture complex dynamics, such as seasonal trends of unseen influence. We define two new metrics: promotion score quantifies the gain in popularity from one unit of promotion for a Youtube video; the loudness level captures the effects of a particular user tweeting about the video. We use the loudness level to compare the effects of a video being promoted by a single highly-followed user (in the top 1% most followed users) against being promoted by a group of mid-followed users. We find that results depend on the type of content being promoted: superusers are more successful in promoting Howto and Gaming videos, whereas the cohort of regular users are more influential for Activism videos. This work provides more accurate and explainable popularity predictions, as well as computational tools for content producers and marketers to allocate resources for promotion campaigns.

Includes members:
  Member "online popularity under promotion viral potential forecasting and the economics of time":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "estimating attention flow in online video networks")
  Member "expecting to be hip hawkes intensity processes for social media popularity":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "nestpp modeling thread dynamics in online discussion forums")
  Member "marked temporal dynamics modeling based on recurrent neural network":
    also connected through other hyperedges:
      (none)
  Member "modeling the intensity function of point process via recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "transformer hawkes process")
  Member "tideh time dependent hawkes process for predicting retweet dynamics":
    also connected through other hyperedges:
      - Hyperedge HE-4 (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-5 (induced by source paper "nestpp modeling thread dynamics in online discussion forums")
  Member "exploring limits to prediction in complex social systems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "popularity prediction on social platforms with coupled graph neural networks")
  Member "recurrent neural networks for driver activity anticipation via sensory fusion architecture":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "sensor fusion gated recurrent fusion to learn driving behavior from temporal multimodal data")

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)