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: "online popularity under promotion viral potential forecasting and the economics of time"
  Abstract: Modeling the popularity dynamics of an online item is an important open problem in computational social science. This paper presents an in-depth study of popularity dynamics under external promotions, especially in predicting popularity jumps of online videos, and determining effective and efficient schedules to promote online content. The recently proposed Hawkes Intensity Process (HIP) models popularity as a non-linear interplay between exogenous stimuli and the endogenous reactions. Here, we propose two novel metrics based on HIP: to describe popularity gain per unit of promotion, and to quantify the time it takes for such effects to unfold. We make increasingly accurate forecasts of future popularity by including information about the intrinsic properties of the video, promotions it receives, and the non-linear effects of popularity ranking. We illustrate by simulation the interplay between the unfolding of popularity over time, and the time-sensitive value of resources. Lastly, our model lends a novel explanation of the commonly adopted periodic and constant promotion strategy in advertising, as increasing the perceived viral potential. This study provides quantitative guidelines about setting promotion schedules considering content virality, timing, and economics.

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"
    - "beyond views measuring and predicting engagement on youtube videos"
    - "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 "toward maximizing the visibility of content in social media brand pages a temporal analysis"):
  with members:
    - "multistage campaigning in social networks"
    - "when to post on social networks"
    - "klout score measuring influence across multiple social networks"
    - "dynamical classes of collective attention in twitter"
    - "trends in social media persistence and decay"
    - "information contagion an empirical study of the spread of news on digg and twitter social networks"
    - "predicting the popularity of online content"

Connected through hyperedge HE-3 (induced by source paper "modeling popularity in asynchronous social media streams with recurrent neural networks"):
  with members:
    - "expecting to be hip hawkes intensity processes for social media popularity"
    - "marked temporal dynamics modeling based on recurrent neural network"
    - "modeling the intensity function of point process via recurrent neural networks"
    - "tideh time dependent hawkes process for predicting retweet dynamics"
    - "feature driven and point process approaches for popularity prediction"
    - "exploring limits to prediction in complex social systems"
    - "recurrent neural networks for driver activity anticipation via sensory fusion architecture"
    - "seismic a self exciting point process model for predicting tweet popularity"

Connected through hyperedge HE-4 (induced by source paper "will this video go viral explaining and predicting the popularity of youtube videos"):
  with members:
    - "beyond views measuring and predicting engagement on youtube videos"
    - "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"

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)