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 "beyond views measuring and predicting engagement on youtube videos"):
  Abstract: The share of videos in the internet traffic has been growing, therefore understanding how videos capture attention on a global scale is also of growing importance. Most current research focus on modeling the number of views, but we argue that video engagement, or time spent watching is a more appropriate measure for resource allocation problems in attention, networking, and promotion activities. In this paper, we present a first large-scale measurement of video-level aggregate engagement from publicly available data streams, on a collection of 5.3 million YouTube videos published over two months in 2016. We study a set of metrics including time and the average percentage of a video watched. We define a new metric, relative engagement, that is calibrated against video properties and strongly correlate with recognized notions of quality. Moreover, we find that engagement measures of a video are stable over time, thus separating the concerns for modeling engagement and those for popularity -- the latter is known to be unstable over time and driven by external promotions. We also find engagement metrics predictable from a cold-start setup, having most of its variance explained by video context, topics and channel information -- R2=0.77. Our observations imply several prospective uses of engagement metrics -- choosing engaging topics for video production, or promoting engaging videos in recommender systems.

Includes members:
  Member "expecting to be hip hawkes intensity processes for social media popularity":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "nestpp modeling thread dynamics in online discussion forums")
  Member "a data driven study of view duration on youtube":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "mi youtube es su youtube analyzing the cultures using youtube thumbnails of popular videos")
  Member "feature driven and point process approaches for popularity prediction":
    also connected through other hyperedges:
      - Hyperedge HE-3 (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-4 (induced by source paper "popularity prediction on social platforms with coupled graph neural networks")
  Member "understanding the detection of view fraud in video content portals":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "4 years of eu cookie law results and lessons learned")
  Member "trendlearner early prediction of popularity trends of user generated content":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "estimating attention flow in online video networks")
  Member "who watches and shares what on youtube and when using twitter to understand youtube viewership":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "estimating attention flow in online video networks")
  Member "can cascades be predicted":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deep collaborative embedding for information cascade prediction")

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)