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 "estimating attention flow in online video networks"):
  Abstract: Online videos have shown tremendous increase in Internet traffic. Most video hosting sites implement recommender systems, which connect the videos into a directed network and conceptually act as a source of pathways for users to navigate. At present, little is known about how human attention is allocated over such large-scale networks, and about the impacts of the recommender systems. In this paper, we first construct the Vevo network -- a YouTube video network with 60,740 music videos interconnected by the recommendation links, and we collect their associated viewing dynamics. This results in a total of 310 million views every day over a period of 9 weeks. Next, we present large-scale measurements that connect the structure of the recommendation network and the video attention dynamics. We use the bow-tie structure to characterize the Vevo network and we find that its core component (23.1% of the videos), which occupies most of the attention (82.6% of the views), is made out of videos that are mainly recommended among themselves. This is indicative of the links between video recommendation and the inequality of attention allocation. Finally, we address the task of estimating the attention flow in the video recommendation network. We propose a model that accounts for the network effects for predicting video popularity, and we show it consistently outperforms the baselines. This model also identifies a group of artists gaining attention because of the recommendation network. Altogether, our observations and our models provide a new set of tools to better understand the impacts of recommender systems on collective social attention.

Includes members:
  Member "offline evaluation of ranking policies with click models":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "eliminating bias in recommender systems via pseudo labeling")
  Member "top k off policy correction for a reinforce recommender system":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "developing multi task recommendations with long term rewards via policy distilled reinforcement learning")
  Member "sir hawkes linking epidemic models and hawkes processes to model diffusions in finite populations":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "sequential recommendation with dual side neighbor based collaborative relation modeling")
  Member "foundations of sequence to sequence modeling for time series":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "forecastnet a time variant deep feed forward neural network architecture for multi step ahead time series forecasting")
  Member "beyond views measuring and predicting engagement on youtube videos":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "will this video go viral explaining and predicting the popularity of youtube videos")
  Member "online popularity under promotion viral potential forecasting and the economics of time":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "toward maximizing the visibility of content in social media brand pages a temporal analysis")
  Member "expecting to be hip hawkes intensity processes for social media popularity":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "nestpp modeling thread dynamics in online discussion forums")
  Member "do cascades recur":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "using network motifs to characterize temporal network evolution leading to diffusion inhibition")

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)