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: "sequential seeding in complex networks trading speed for coverage"
  Abstract: Diffusion in complex networks studied in the paper is a process of spreading information, ideas or substances with replication between nodes through edges from the regions of high to low concentration. Diffusion cascades are triggered by the activation of a small set of initial nodes - seeds - and later supported by the natural process. In this work, several novel approaches related to extension of the commonly used seeding strategies into a sequence of stages are proposed. Sequential seeding method is compared with a single stage approach using both real and artificial complex networks and applying various dynamic node ranking methods and diffusion parameters. The experimental results show that sequential seeding strategies deliver better results than single stage seeding in most cases. These strategies avoid seeding nodes that are activated through the natural diffusion process at the preceding stages of sequential seeding. The gain arises when a saved seed is allocated to a node difficult to reach via diffusion process. The performance is further improved by identifying non-activated nodes with high potential of activizing their not yet active neighbors. It is boosted by adaptive recomputing of network measures on the subgraph with non-activated nodes only. The higher length of seeding sequences tends to activate more nodes but extends the duration of diffusion process. The results demonstrate the trade-off between the coverage and speed of diffusion that can be balanced according to the importance assigned to coverage and time in specific applications.

Connected through hyperedge HE-1 (induced by source paper "influence maximization for fixed heterogeneous thresholds"):
  with members:
    - "balancing speed and coverage by sequential seeding in complex networks"
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "a scalable heuristic for viral marketing under the tipping model"

Connected through hyperedge HE-2 (induced by source paper "latent viral marketing concepts and control methods"):
  with members:
    - "information is not a virus and other consequences of human cognitive limits"
    - "a scalable heuristic for viral marketing under the tipping model"
    - "the role of social networks in information diffusion"

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)