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: "identifying a set of influential spreaders in complex networks"
  Abstract: Identifying a set of influential spreaders in complex networks plays a crucial role in effective information spreading. A simple strategy is to choose top-$r$ ranked nodes as spreaders according to influence ranking method such as PageRank, ClusterRank and $k$-shell decomposition. Besides, some heuristic methods such as hill-climbing, SPIN, degree discount and independent set based are also proposed. However, these approaches suffer from a possibility that some spreaders are so close together that they overlap sphere of influence or time consuming. In this report, we present a simply yet effectively iterative method named VoteRank to identify a set of decentralized spreaders with the best spreading ability. In this approach, all nodes vote in a spreader in each turn, and the voting ability of neighbors of elected spreader will be decreased in subsequent turn. Experimental results on four real networks show that under Susceptible-Infected-Recovered (SIR) model, VoteRank outperforms the traditional benchmark methods on both spreading speed and final affected scale. What's more, VoteRank is also superior to other group-spreader identifying methods on computational time.

Connected through hyperedge HE-1 (induced by source paper "computational models for commercial advertisements in social networks"):
  with members:
    - "defining and evaluating network communities based on ground truth"

Connected through hyperedge HE-2 (induced by source paper "group based centrality for immunization of complex networks"):
  with members:
    - "centrality measures for networks with community structure"
    - "how good is the shapley value based approach to the influence maximization problem"
    - "efficient computation of the shapley value for game theoretic network centrality"

Connected through hyperedge HE-3 (induced by source paper "seed selection for information cascade in multilayer networks"):
  with members:
    - "do we really need to catch them all a new user guided social media crawling method"
    - "finding influential users in social media using association rule learning"
    - "spreading processes in multilayer networks"

Connected through hyperedge HE-4 (induced by source paper "seeds buffering for information spreading processes"):
  with members:
    - "balancing speed and coverage by sequential seeding in complex networks"
    - "scalable methods for adaptively seeding a social network"
    - "seed selection for spread of influence in social networks temporal vs static approach"

Connected through hyperedge HE-5 (induced by source paper "balancing speed and coverage by sequential seeding in complex networks"):
  with members:
    - "scalable methods for adaptively seeding a social network"
    - "seed selection for spread of influence in social networks temporal vs static approach"

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)