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: "seed selection for spread of influence in social networks temporal vs static approach"
  Abstract: The problem of finding optimal set of users for influencing others in the social network has been widely studied. Because it is NP-hard, some heuristics were proposed to find sub-optimal solutions. Still, one of the commonly used assumption is the one that seeds are chosen on the static network, not the dynamic one. This static approach is in fact far from the real-world networks, where new nodes may appear and old ones dynamically disappear in course of time. The main purpose of this paper is to analyse how the results of one of the typical models for spread of influence - linear threshold - differ depending on the strategy of building the social network used later for choosing seeds. To show the impact of network creation strategy on the final number of influenced nodes - outcome of spread of influence, the results for three approaches were studied: one static and two temporal with different granularities, i.e. various number of time windows. Social networks for each time window encapsulated dynamic changes in the network structure. Calculation of various node structural measures like degree or betweenness respected these changes by means of forgetting mechanism - more recent data had greater influence on node measure values. These measures were, in turn, used for node ranking and their selection for seeding. All concepts were applied to experimental verification on five real datasets. The results revealed that temporal approach is always better than static and the higher granularity in the temporal social network while seeding, the more finally influenced nodes. Additionally, outdegree measure with exponential forgetting typically outperformed other time-dependent structural measures, if used for seed candidate ranking.

Connected through hyperedge HE-1 (induced by source paper "evolving influence maximization in evolving networks"):
  with members:
    - "adaptive influence maximization in dynamic social networks"
    - "time is what prevents everything from happening at once propagation time conscious influence maximization"
    - "outward influence and cascade size estimation in billion scale networks"
    - "online influence maximization in non stationary social networks"
    - "boosting information spread an algorithmic approach"
    - "online influence maximization"
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "sketch based influence maximization and computation scaling up with guarantees"

Connected through hyperedge HE-2 (induced by source paper "probing limits of information spread with sequential seeding"):
  with members:
    - "seeds buffering for information spreading processes"
    - "balancing speed and coverage by sequential seeding in complex networks"
    - "scalable methods for adaptively seeding a social network"

Connected through hyperedge HE-3 (induced by source paper "using node centrality and optimal control to maximize information diffusion in social networks"):
  with members:
    - "optimal control of information epidemics modeled as maki thompson rumors"
    - "how to run a campaign optimal control of sis and sir information epidemics"
    - "influence diffusion dynamics and influence maximization in social networks with friend and foe relationships"

Connected through hyperedge HE-4 (induced by source paper "seeds buffering for information spreading processes"):
  with members:
    - "identifying a set of influential spreaders in complex networks"
    - "balancing speed and coverage by sequential seeding in complex networks"
    - "scalable methods for adaptively seeding a social network"

Connected through hyperedge HE-5 (induced by source paper "a picture is worth a thousand words an empirical study on the influence of content visibility on diffusion processes within a virtual world"):
  with members:
    - "spreading processes in multilayer networks"
    - "knowledge workers collaborative learning behavior modeling in an organizational social network"
    - "knowledge acquisition from social platforms based on network distributions fitting"
    - "model of multilayer knowledge diffusion for competence development in an organization"
    - "extraction of multilayered social networks from activity data"
    - "multi layered social networks"
    - "analysis of neighbourhoods in multi layered dynamic social networks"
    - "the online laboratory conducting experiments in a real labor market"

Connected through hyperedge HE-6 (induced by source paper "sequential seeding in complex networks trading speed for coverage"):
  with members:
    - "scalable methods for adaptively seeding a social network"
    - "software systems through complex networks science review analysis and applications"

Connected through hyperedge HE-7 (induced by source paper "balancing speed and coverage by sequential seeding in complex networks"):
  with members:
    - "identifying a set of influential spreaders in complex networks"
    - "scalable methods for adaptively seeding a social network"

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)