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: "time matters multi scale temporalization of social media popularity"
  Abstract: The evolution of social media popularity exhibits rich temporality, i.e., popularities change over time at various levels of temporal granularity. This is influenced by temporal variations of public attentions or user activities. For example, popularity patterns of street snap on Flickr are observed to depict distinctive fashion styles at specific time scales, such as season-based periodic fluctuations for Trench Coat or one-off peak in days for Evening Dress. However, this fact is often overlooked by existing research of popularity modeling. We present the first study to incorporate multiple time-scale dynamics into predicting online popularity. We propose a novel computational framework in the paper, named Multi-scale Temporalization, for estimating popularity based on multi-scale decomposition and structural reconstruction in a tensor space of user, post, and time by joint low-rank constraints. By considering the noise caused by context inconsistency, we design a data rearrangement step based on context aggregation as preprocessing to enhance contextual relevance of neighboring data in the tensor space. As a result, our approach can leverage multiple levels of temporal characteristics and reduce the noise of data decomposition to improve modeling effectiveness. We evaluate our approach on two large-scale Flickr image datasets with over 1.8 million photos in total, for the task of popularity prediction. The results show that our approach significantly outperforms state-of-the-art popularity prediction techniques, with a relative improvement of 10.9%-47.5% in terms of prediction accuracy.

Connected through hyperedge HE-1 (induced by source paper "user aware folk popularity rank user popularity based tag recommendation that can enhance social popularity"):
  with members:
    - "stacked cross attention for image text matching"
    - "a multimodal approach to predict social media popularity"
    - "tagging like humans diverse and distinct image annotation"
    - "pay attention to virality understanding popularity of social media videos with the attention mechanism"
    - "modeling popularity in asynchronous social media streams with recurrent neural networks"
    - "neural collaborative filtering"
    - "use of deep learning in modern recommendation system a summary of recent works"
    - "stack captioning coarse to fine learning for image captioning"

Connected through hyperedge HE-2 (induced by source paper "intrinsic image popularity assessment"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "how to become instagram famous post popularity prediction with dual attention"
    - "pay attention to virality understanding popularity of social media videos with the attention mechanism"
    - "sequential prediction of social media popularity with deep temporal context networks"
    - "cats and captions vs creators and the clock comparing multimodal content to context in predicting relative popularity"
    - "modeling image virality with pairwise spatial transformer networks"
    - "dipiq blind image quality assessment by learning to rank discriminable image pairs"
    - "predicting popularity of online videos using support vector regression"

Connected through hyperedge HE-3 (induced by source paper "smp challenge an overview of social media prediction challenge 2019"):
  with members:
    - "sequential prediction of social media popularity with deep temporal context networks"
    - "tideh time dependent hawkes process for predicting retweet dynamics"
    - "yelp dataset challenge review rating prediction"
    - "visual genome connecting language and vision using crowdsourced dense image annotations"
    - "predictability of popularity gaps between prediction and understanding"
    - "exploring limits to prediction in complex social systems"
    - "seismic a self exciting point process model for predicting tweet popularity"
    - "online popularity and topical interests through the lens of instagram"

Connected through hyperedge HE-4 (induced by source paper "how to become instagram famous post popularity prediction with dual attention"):
  with members:
    - "rumor detection and classification for twitter data"
    - "improved fusion of visual and language representations by dense symmetric co attention for visual question answering"
    - "multimodal residual learning for visual qa"
    - "hierarchical question image co attention for visual question answering"
    - "dynamic coattention networks for question answering"
    - "show attend and tell neural image caption generation with visual attention"
    - "deep residual learning for image recognition"
    - "a picture tells a thousand words about you user interest profiling from user generated visual content"

Connected through hyperedge HE-5 (induced by source paper "a multimodal approach to predict social media popularity"):
  with members:
    - "sequential prediction of social media popularity with deep temporal context networks"
    - "predicting popularity of online videos using support vector regression"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-6 (induced by source paper "an iterative refinement approach for social media headline prediction"):
  with members:
    - "sequential prediction of social media popularity with deep temporal context networks"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-7 (induced by source paper "cats and captions vs creators and the clock comparing multimodal content to context in predicting relative popularity"):
  with members:
    - "science askscience and badscience on the coexistence of highly related communities"
    - "item popularity prediction in e commerce using image quality feature vectors"
    - "beyond exchangeability the chinese voting process"
    - "learning distributed representations of sentences from unlabelled data"
    - "understanding image virality"
    - "all who wander on the prevalence and characteristics of multi community engagement"
    - "deep residual learning for image recognition"
    - "an image is worth more than a thousand favorites surfacing the hidden beauty of flickr pictures"

Connected through hyperedge HE-8 (induced by source paper "predicting popularity of online videos using support vector regression"):
  with members:
    - "deep residual learning for image recognition"
    - "forecasting popularity of videos using social media"
    - "on the dynamics of social media popularity a youtube case study"
    - "characterizing the life cycle of online news stories using social media reactions"
    - "the pulse of news in social media forecasting popularity"
    - "faster and better a machine learning approach to corner detection"
    - "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)