human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "time matters multi scale temporalization of social media popularity":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "user aware folk popularity rank user popularity based tag recommendation that can enhance social popularity")
  Member "deep residual learning for image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "forecasting popularity of videos using social media":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "online convex optimization for caching networks")
  Member "on the dynamics of social media popularity a youtube case study":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "curating social media data")
  Member "characterizing the life cycle of online news stories using social media reactions":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "scilens evaluating the quality of scientific news articles using social media and scientific literature indicators")
  Member "the pulse of news in social media forecasting popularity":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "modeling the popularity of twitter hashtags with master equations")
  Member "faster and better a machine learning approach to corner detection":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a comparative study for non rigid image registration and rigid image registration")
  Member "predicting the popularity of online content":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "online convex optimization for caching networks")

Question: Please tell me which class the source paper 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)