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: "popular topics spread faster new dimension for influence propagation in online social networks"
  Abstract: Although influence propagation of Online Social Networks (OSNs) is widely studied in literature, none of the existing works can cope with the situation that the propagation rate dynamically increases for popular topics. Instead, they all assumed known rates (either constants or drawn from known distributions) for influence propagation models. However, such models cannot correctly describe influence diffusion in reality. #R##N#In this paper, we propose a novel model, Dynamic Influence Propagation (DIP), that allows propagation rate to change during the diffusion, based on which we define a new problem: Threshold Activation Problem under DIP (TAP-DIP), which adds another layer of complexity over the already \#P-Hard TAP problem. Despite the hardness of TAP-DIP, we are able to design a Lipschitz Optimization scheme (LOS) that can solve TAP-DIP with $O(\log|V|)$ ratio where $V$ is the set of nodes in the OSN. Using various real OSN datasets, we experimentally demonstrate that LOS not only generates high-quality yet much smaller seed sets when being aware of the rate increase, but also scalable.

Connected through hyperedge HE-1 (induced by source paper "multiplex influence maximization in online social networks with heterogeneous diffusion models"):
  with members:
    - "why approximate when you can get the exact optimal targeted viral marketing at scale"
    - "scalable bicriteria algorithms for the threshold activation problem in online social networks"
    - "stop and stare optimal sampling algorithms for viral marketing in billion scale networks"
    - "sketch based influence maximization and computation scaling up with guarantees"

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)