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: "virality prediction and community structure in social networks"
  Abstract: How does network structure affect diffusion? Recent studies suggest that the answer depends on the type of contagion. Complex contagions, unlike infectious diseases (simple contagions), are affected by social reinforcement and homophily. Hence, the spread within highly clustered communities is enhanced, while diffusion across communities is hampered. A common hypothesis is that memes and behaviors are complex contagions. We show that, while most memes indeed spread like complex contagions, a few viral memes spread across many communities, like diseases. We demonstrate that the future popularity of a meme can be predicted by quantifying its early spreading pattern in terms of community concentration. The more communities a meme permeates, the more viral it is. We present a practical method to translate data about community structure into predictive knowledge about what information will spread widely. This connection contributes to our understanding in computational social science, social media analytics, and marketing applications.

Connected through hyperedge HE-1 (induced by source paper "deep collaborative embedding for information cascade prediction"):
  with members:
    - "dyngraph2vec capturing network dynamics using dynamic graph representation learning"
    - "iad interaction aware diffusion framework in social networks"
    - "deepinf social influence prediction with deep learning"
    - "attributed social network embedding"
    - "collaborative inference of coexisting information diffusions"
    - "a survey on network embedding"
    - "a comprehensive survey of graph embedding problems techniques and applications"
    - "topological recurrent neural network for diffusion prediction"

Connected through hyperedge HE-2 (induced by source paper "modeling the popularity of twitter hashtags with master equations"):
  with members:
    - "influence of fake news in twitter during the 2016 us presidential election"
    - "validation of twitter opinion trends with national polling aggregates hillary clinton vs donald trump"
    - "modeling the infectiousness of twitter hashtags"
    - "the pulse of news in social media forecasting popularity"
    - "stochastic models of user contributory web sites"
    - "novelty and collective attention"

Connected through hyperedge HE-3 (induced by source paper "online reactions to the 2017 unite the right rally in charlottesville measuring polarization in twitter networks using media followership"):
  with members:
    - "social network analysis for social neuroscientists"
    - "its all in a name detecting and labeling bots by their name"
    - "centrality in modular networks"
    - "a quantitative approach to understanding online antisemitism"
    - "who let the trolls out towards understanding state sponsored trolls"
    - "on the origins of memes by means of fringe web communities"
    - "a long term analysis of polarization on twitter"
    - "botornot a system to evaluate social bots"

Connected through hyperedge HE-4 (induced by source paper "the opacity problem in social contagion"):
  with members:
    - "information is not a virus and other consequences of human cognitive limits"
    - "node2vec scalable feature learning for networks"
    - "can cascades be predicted"
    - "geotagging one hundred million twitter accounts with total variation minimization"
    - "distributed representations of words and phrases and their compositionality"
    - "social influence in social advertising evidence from field experiments"
    - "what stops social epidemics"

Connected through hyperedge HE-5 (induced by source paper "interactional and informational attention on twitter"):
  with members:
    - "local variation of hashtag spike trains and popularity in twitter"
    - "when is it biased assessing the representativeness of twitter s streaming api"
    - "rising tides or rising stars dynamics of shared attention on twitter during media events"
    - "the bursty dynamics of the twitter information network"
    - "dynamical classes of collective attention in twitter"
    - "novelty and collective attention"

Connected through hyperedge HE-6 (induced by source paper "l2p an algorithm for estimating heavy tailed outcomes"):
  with members:
    - "ell_1 regression with heavy tailed distributions"
    - "xgboost a scalable tree boosting system"
    - "adam a method for stochastic optimization"
    - "multiplicative attribute graph model of real world networks"
    - "smote synthetic minority over sampling technique"
    - "learning to order things"

Connected through hyperedge HE-7 (induced by source paper "an experimental study of structural diversity in social networks"):
  with members:
    - "the lifecycles of apps in a social ecosystem"
    - "popularity prediction in microblogging network a case study on sina weibo"
    - "characterizing and curating conversation threads expansion focus volume re entry"
    - "social influence in social advertising evidence from field experiments"
    - "on the interplay between social and topical structure"

Connected through hyperedge HE-8 (induced by source paper "memesequencer sparse matching for embedding image macros"):
  with members:
    - "modeling image virality with pairwise spatial transformer networks"
    - "information evolution in social networks"
    - "do cascades recur"
    - "a corpus and evaluation framework for deeper understanding of commonsense stories"
    - "end to end localization and ranking for relative attributes"
    - "tensorflow a system for large scale machine learning"
    - "understanding image virality"
    - "skip thought vectors"

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)