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: "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.

Connected through hyperedge HE-1 (induced by source paper "estimating attention flow in online video networks"):
  with members:
    - "offline evaluation of ranking policies with click models"
    - "top k off policy correction for a reinforce recommender system"
    - "sir hawkes linking epidemic models and hawkes processes to model diffusions in finite populations"
    - "foundations of sequence to sequence modeling for time series"
    - "online popularity under promotion viral potential forecasting and the economics of time"
    - "expecting to be hip hawkes intensity processes for social media popularity"
    - "do cascades recur"
    - "exploring limits to prediction in complex social systems"

Connected through hyperedge HE-2 (induced by source paper "will this video go viral explaining and predicting the popularity of youtube videos"):
  with members:
    - "online popularity under promotion viral potential forecasting and the economics of time"
    - "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"
    - "seismic a self exciting point process model for predicting tweet popularity"
    - "characterizing the life cycle of online news stories using social media reactions"

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"
    - "affect recognition in ads with application to computational advertising"
    - "evaluating content centric vs user centric ad affect recognition"
    - "a data driven study of view duration on youtube"

Connected through hyperedge HE-4 (induced by source paper "prediction and localization of student engagement in the wild"):
  with members:
    - "deep multi instance networks with sparse label assignment for whole mammogram classification"
    - "daisee towards user engagement recognition in the wild"

Connected through hyperedge HE-5 (induced by source paper "proactive video chunks caching and processing for latency and cost minimization in edge networks"):
  with members:
    - "collaborative multi bitrate video caching and processing in mobile edge computing networks"

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.SI(Social and Information Networks)