human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "a data driven study of view duration on youtube"
  Abstract: Video watching had emerged as one of the most frequent media activities on the Internet. Yet, little is known about how users watch online video. Using two distinct YouTube datasets, a set of random YouTube videos crawled from the Web and a set of videos watched by participants tracked by a Chrome extension, we examine whether and how indicators of collective preferences and reactions are associated with view duration of videos. We show that video view duration is positively associated with the video's view count, the number of likes per view, and the negative sentiment in the comments. These metrics and reactions have a significant predictive power over the duration the video is watched by individuals. Our findings provide a more precise understandings of user engagement with video content in social media beyond view count.

Connected through hyperedge HE-1 (induced by source paper "mi youtube es su youtube analyzing the cultures using youtube thumbnails of popular videos"):
  with members:
    - "cultural values and cross cultural video consumption on youtube"
    - "cultural diffusion and trends in facebook photographs"
    - "tall temporal activity localization via language query"
    - "localizing moments in video with natural language"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "microsoft coco common objects in context"

Connected through hyperedge HE-2 (induced by source paper "tubes bubbles topological confinement of youtube recommendations"):
  with members:
    - "algorithmic distortion of informational landscapes"
    - "evolution of ego networks in social media with link recommendations"
    - "users polarization on facebook and youtube"

Connected through hyperedge HE-3 (induced by source paper "engagement estimation in advertisement videos with eeg"):
  with members:
    - "looking beyond a clever narrative visual context and attention are primary drivers of affect in video advertisements"
    - "beyond views measuring and predicting engagement on youtube videos"
    - "affect recognition in ads with application to computational advertising"
    - "evaluating content centric vs user centric ad affect recognition"

Connected through hyperedge HE-4 (induced by source paper "beyond views measuring and predicting engagement on youtube videos"):
  with members:
    - "expecting to be hip hawkes intensity processes for social media popularity"
    - "feature driven and point process approaches for popularity prediction"
    - "exploring limits to prediction in complex social systems"
    - "understanding the detection of view fraud in video content portals"
    - "trendlearner early prediction of popularity trends of user generated content"
    - "who watches and shares what on youtube and when using twitter to understand youtube viewership"
    - "can cascades be predicted"
    - "fake view analytics in online video services"

Connected through hyperedge HE-5 (induced by source paper "cultural values and cross cultural video consumption on youtube"):
  with members:
    - "understanding musical diversity via online social media"
    - "how do global audiences take shape the role of institutions and culture in patterns of web use"

Question: Please tell me which class the center node 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.HC(Human-Computer Interaction)