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: "predicting popularity of online videos using support vector regression"
  Abstract: In this work, we propose a regression method to predict the popularity of an online video measured by its number of views. Our method uses Support Vector Regression with Gaussian radial basis functions. We show that predicting popularity patterns with this approach provides more precise and more stable prediction results, mainly thanks to the nonlinear character of the proposed method as well as its robustness. We prove the superiority of our method against the state of the art using datasets containing almost 24 000 videos from YouTube and Facebook. We also show that using visual features, such as the outputs of deep neural networks or scene dynamics’ metrics, can be useful for popularity prediction before content publication. Furthermore, we show that popularity prediction accuracy can be improved by combining early distribution patterns with social and visual features and that social features represent a much stronger signal in terms of video popularity prediction than the visual ones.

Connected through hyperedge HE-1 (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"
    - "time matters multi scale temporalization of social media popularity"
    - "dipiq blind image quality assessment by learning to rank discriminable image pairs"

Connected through hyperedge HE-2 (induced by source paper "pay attention to virality understanding popularity of social media videos with the attention mechanism"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "recurrent neural networks for online video popularity prediction"
    - "viraliency pooling local virality"
    - "shallow reading with deep learning predicting popularity of online content using only its title"
    - "understanding image virality"
    - "show attend and tell neural image caption generation with visual attention"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (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"
    - "time matters multi scale temporalization of social media popularity"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-4 (induced by source paper "music popularity metrics characteristics and audio based prediction"):
  with members:
    - "revisiting the problem of audio based hit song prediction using convolutional neural networks"
    - "unravelling the impact of temporal and geographical locality in content caching systems"
    - "measuring the evolution of contemporary western popular music"

Connected through hyperedge HE-5 (induced by source paper "shallow reading with deep learning predicting popularity of online content using only its title"):
  with members:
    - "stop clickbait detecting and preventing clickbaits in online news media"
    - "breakingnews article annotation by image and text processing"
    - "text understanding from scratch"
    - "a c lstm neural network for text classification"
    - "adam a method for stochastic optimization"
    - "characterizing the life cycle of online news stories using social media reactions"
    - "the pulse of news in social media forecasting popularity"
    - "natural language processing almost from scratch"

Connected through hyperedge HE-6 (induced by source paper "engagement dynamics and sensitivity analysis of youtube videos"):
  with members:
    - "deep learning in neural networks"
    - "forecasting popularity of videos in youtube"

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)