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: "information diffusion and external influence in networks"
  Abstract: Social networks play a fundamental role in the diffusion of information. However, there are two different ways of how information reaches a person in a network. Information reaches us through connections in our social networks, as well as through the influence of external out-of-network sources, like the mainstream media. While most present models of information adoption in networks assume information only passes from a node to node via the edges of the underlying network, the recent availability of massive online social media data allows us to study this process in more detail. We present a model in which information can reach a node via the links of the social network or through the influence of external sources. We then develop an efficient model parameter fitting technique and apply the model to the emergence of URL mentions in the Twitter network. Using a complete one month trace of Twitter we study how information reaches the nodes of the network. We quantify the external influences over time and describe how these influences affect the information adoption. We discover that the information tends to "jump" across the network, which can only be explained as an effect of an unobservable external influence on the network. We find that only about 71% of the information volume in Twitter can be attributed to network diffusion, and the remaining 29% is due to external events and factors outside the network.

Connected through hyperedge HE-1 (induced by source paper "modeling information diffusion in online social networks with partial differential equations"):
  with members:
    - "diffusive logistic model towards predicting information diffusion in online social networks"
    - "on the interplay between social and topical structure"
    - "what stops social epidemics"
    - "information contagion an empirical study of the spread of news on digg and twitter social networks"
    - "a framework for quantitative analysis of cascades on networks"

Connected through hyperedge HE-2 (induced by source paper "learning graphs from noisy epidemic cascades"):
  with members:
    - "the cost of uncertainty in curing epidemics"
    - "fake news mitigation via point process based intervention"
    - "hiding the rumor source"
    - "correlated cascades compete or cooperate"
    - "a lower bound on the performance of dynamic curing policies for epidemics on graphs"
    - "seismic a self exciting point process model for predicting tweet popularity"
    - "spy vs spy rumor source obfuscation"
    - "an efficient curing policy for epidemics on graphs"

Connected through hyperedge HE-3 (induced by source paper "ghostlink latent network inference for influence aware recommendation"):
  with members:
    - "from amateurs to connoisseurs modeling the evolution of user expertise through online reviews"
    - "the author topic model for authors and documents"
    - "uncovering the temporal dynamics of diffusion networks"
    - "inferring networks of diffusion and influence"
    - "on the convexity of latent social network inference"

Connected through hyperedge HE-4 (induced by source paper "leveraging motifs to model the temporal dynamics of diffusion networks"):
  with members:
    - "understanding and forecasting lifecycle events in information cascades"
    - "uncovering the temporal dynamics of diffusion networks"
    - "inferring networks of diffusion and influence"

Connected through hyperedge HE-5 (induced by source paper "impact of social influence on adoption behavior an online controlled experimental evaluation"):
  with members:
    - "cumulative effect in information diffusion empirical study on a microblogging network"
    - "what stops social epidemics"

Connected through hyperedge HE-6 (induced by source paper "disentangling sources of influence in online social networks"):
  with members:
    - "the anatomy of the facebook social graph"

Connected through hyperedge HE-7 (induced by source paper "deepinf social influence prediction with deep learning"):
  with members:
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "inductive representation learning on large graphs"
    - "attention is all you need"
    - "a century of science globalization of scientific collaborations citations and innovations"
    - "representation learning on graphs methods and applications"
    - "instance normalization the missing ingredient for fast stylization"
    - "structural diversity and homophily a study across more than one hundred big networks"
    - "node2vec scalable feature learning for networks"

Connected through hyperedge HE-8 (induced by source paper "tracking influential nodes in time decaying dynamic interaction networks"):
  with members:
    - "real time influence maximization on dynamic social streams"
    - "tracking influential individuals in dynamic networks"
    - "submodular optimization over sliding windows"
    - "robust influence maximization"
    - "boosting information spread an algorithmic approach"
    - "submodular maximization over sliding windows"
    - "online influence maximization"
    - "influence maximization near optimal time complexity meets practical efficiency"

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)