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: "least cost influence maximization across multiple social networks"
  Abstract: Recently, in online social networks (OSNs), the least cost influence (LCI) problem has become one of the central research topics. It aims at identifying a minimum number of seed users who can trigger a wide cascade of information propagation. Most of existing literature investigated the LCI problem only based on an individual network. However, nowadays users often join several OSNs such that information could be spread across different networks simultaneously. Therefore, in order to obtain the best set of seed users, it is crucial to consider the role of overlapping users under this circumstances. In this article, we propose a unified framework to represent and analyze the influence diffusion in multiplex networks. More specifically, we tackle the LCI problem by mapping a set of networks into a single one via lossless and lossy coupling schemes. The lossless coupling scheme preserves all properties of original networks to achieve high-quality solutions, while the lossy coupling scheme offers an attractive alternative when the running time and memory consumption are of primary concern. Various experiments conducted on both real and synthesized datasets have validated the effectiveness of the coupling schemes, which also provide some interesting insights into the process of influence propagation in multiplex networks.

Connected through hyperedge HE-1 (induced by source paper "diffusion on dynamic contact networks with indirect transmission links"):
  with members:
    - "lightweight mobile ad hoc network routing protocols for smartphones"
    - "impact of indirect contacts in emerging infectious disease on social networks"
    - "diffusion in colocation contact networks the impact of nodal spatiotemporal dynamics"
    - "an introduction to temporal graphs an algorithmic perspective"
    - "common neighbours and the local community paradigm for topological link prediction in bipartite networks"
    - "latent self exciting point process model for spatial temporal networks"
    - "time varying graphs and dynamic networks"
    - "information propagation speed in mobile and delay tolerant networks"

Connected through hyperedge HE-2 (induced by source paper "social influence and radicalization a social data analytics study"):
  with members:
    - "adaptive influence maximization in dynamic social networks"
    - "data curation apis"
    - "failedrevolutions using twitter to study the antecedents of isis support"
    - "klout score measuring influence across multiple social networks"
    - "online social media in the syria conflict encompassing the extremes and the in betweens"
    - "big data and cross document coreference resolution current state and future opportunities"
    - "maximizing social influence in nearly optimal time"
    - "on budgeted influence maximization in social networks"

Connected through hyperedge HE-3 (induced by source paper "importance sample based approximation algorithm for cost aware targeted viral marketing"):
  with members:
    - "importance sketching of influence dynamics in billion scale networks"
    - "why approximate when you can get the exact optimal targeted viral marketing at scale"
    - "stop and stare optimal sampling algorithms for viral marketing in billion scale networks"
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "maximizing social influence in nearly optimal time"
    - "defining and evaluating network communities based on ground truth"

Connected through hyperedge HE-4 (induced by source paper "cost aware targeted viral marketing approximation with less samples"):
  with members:
    - "importance sketching of influence dynamics in billion scale networks"
    - "why approximate when you can get the exact optimal targeted viral marketing at scale"
    - "stop and stare optimal sampling algorithms for viral marketing in billion scale networks"
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "maximizing social influence in nearly optimal time"
    - "defining and evaluating network communities based on ground truth"

Connected through hyperedge HE-5 (induced by source paper "scalable bicriteria algorithms for the threshold activation problem in online social networks"):
  with members:
    - "stop and stare optimal sampling algorithms for viral marketing in billion scale networks"
    - "combining traditional marketing and viral marketing with amphibious influence maximization"
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "the bursty dynamics of the twitter information network"
    - "sketch based influence maximization and computation scaling up with guarantees"
    - "maximizing social influence in nearly optimal time"
    - "information diffusion and external influence in networks"
    - "leveraging discarded samples for tighter estimation of multiple set aggregates"

Connected through hyperedge HE-6 (induced by source paper "cost effective seed selection in online social networks"):
  with members:
    - "scalable bicriteria algorithms for the threshold activation problem in online social networks"
    - "boosting information spread an algorithmic approach"
    - "stop and stare optimal sampling algorithms for viral marketing in billion scale networks"
    - "minimizing seed set selection with probabilistic coverage guarantee in a social network"
    - "sketch based influence maximization and computation scaling up with guarantees"

Connected through hyperedge HE-7 (induced by source paper "why approximate when you can get the exact optimal targeted viral marketing at scale"):
  with members:
    - "stop and stare optimal sampling algorithms for viral marketing in billion scale networks"
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "sketch based influence maximization and computation scaling up with guarantees"
    - "maximizing social influence in nearly optimal time"
    - "on budgeted influence maximization in social networks"

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)