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: "popularity prediction in microblogging network a case study on sina weibo"
  Abstract: Predicting the popularity of content is important for both the host and users of social media sites. The challenge of this problem comes from the inequality of the popularity of con- tent. Existing methods for popularity prediction are mainly based on the quality of content, the interface of social media site to highlight contents, and the collective behavior of user- s. However, little attention is paid to the structural charac- teristics of the networks spanned by early adopters, i.e., the users who view or forward the content in the early stage of content dissemination. In this paper, taking the Sina Weibo as a case, we empirically study whether structural character- istics can provide clues for the popularity of short messages. We find that the popularity of content is well reflected by the structural diversity of the early adopters. Experimental results demonstrate that the prediction accuracy is signif- icantly improved by incorporating the factor of structural diversity into existing methods.

Connected through hyperedge HE-1 (induced by source paper "an experimental study of structural diversity in social networks"):
  with members:
    - "the lifecycles of apps in a social ecosystem"
    - "characterizing and curating conversation threads expansion focus volume re entry"
    - "virality prediction and community structure in social networks"
    - "social influence in social advertising evidence from field experiments"
    - "on the interplay between social and topical structure"

Connected through hyperedge HE-2 (induced by source paper "modeling and predicting popularity dynamics via deep learning attention mechanism"):
  with members:
    - "towards binary valued gates for robust lstm training"
    - "attention is all you need"
    - "modeling the intensity function of point process via recurrent neural networks"
    - "retain interpretable predictive model in healthcare using reverse time attention mechanism"
    - "modeling and predicting popularity dynamics of microblogs using self excited hawkes processes"
    - "feed forward networks with attention can solve some long term memory problems"
    - "describing multimedia content using attention based encoder decoder networks"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-3 (induced by source paper "weseer visual analysis for better information cascade prediction of wechat articles"):
  with members:
    - "modeling and predicting popularity dynamics of microblogs using self excited hawkes processes"
    - "the lifecycle and cascade of wechat social messaging groups"
    - "seismic a self exciting point process model for predicting tweet popularity"
    - "can cascades be predicted"
    - "cumulative effect in information diffusion empirical study on a microblogging network"
    - "the pulse of news in social media forecasting popularity"
    - "structure and dynamics of information pathways in online media"

Connected through hyperedge HE-4 (induced by source paper "predicting the popularity of topics based on user sentiment in microblogging websites"):
  with members:
    - "twitter mood predicts the stock market"
    - "using a model of social dynamics to predict popularity of news"
    - "modeling public mood and emotion twitter sentiment and socio economic phenomena"
    - "predicting the popularity of online content"

Connected through hyperedge HE-5 (induced by source paper "breakingnews article annotation by image and text processing"):
  with members:
    - "planet photo geolocation with convolutional neural networks"
    - "whittlesearch interactive image search with relative attribute feedback"
    - "explicit knowledge based reasoning for visual question answering"
    - "learning deep structure preserving image text embeddings"
    - "structured prediction with output embeddings for semantic image annotation"
    - "learning transferable features with deep adaptation networks"
    - "densecap fully convolutional localization networks for dense captioning"
    - "how transferable are features in deep neural networks"

Connected through hyperedge HE-6 (induced by source paper "online social network analysis a survey of research applications in computer science"):
  with members:
    - "inheritance patterns in citation networks reveal scientific memes"
    - "who will retweet this automatically identifying and engaging strangers on twitter to spread information"
    - "networkit an interactive tool suite for high performance network analysis"
    - "networkit a tool suite for large scale complex network analysis"
    - "can cascades be predicted"
    - "exploring image virality in google plus"
    - "a scalable heuristic for viral marketing under the tipping model"
    - "a meta analysis of state of the art electoral prediction from twitter data"

Connected through hyperedge HE-7 (induced by source paper "temporal scaling in information propagation"):
  with members:
    - "predicting adoption probabilities in social networks"
    - "staticgreedy solving the scalability accuracy dilemma in influence maximization"
    - "quantifying long term scientific impact"
    - "structure and dynamics of information pathways in online media"
    - "uncovering the temporal dynamics of diffusion networks"
    - "inferring networks of diffusion and influence"
    - "using a model of social dynamics to predict popularity of news"
    - "novelty and collective attention"

Connected through hyperedge HE-8 (induced by source paper "forecasting popularity of videos using social media"):
  with members:
    - "distributed online learning in social recommender systems"
    - "efficient optimal learning for contextual bandits"
    - "using a model of social dynamics to predict popularity of news"
    - "contextual bandits with similarity information"
    - "predicting the popularity of online content"

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)