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: "modeling social networks with overlapping communities using hypergraphs and their line graphs"
  Abstract: We propose that hypergraphs can be used to model social networks with overlapping communities. The nodes of the hypergraphs represent the communities. The hyperlinks of the hypergraphs denote the individuals who may participate in multiple communities. The hypergraphs are not easy to analyze, however, the line graphs of hypergraphs are simple graphs or weighted graphs, so that the network theory can be applied. We define the overlapping depth $k$ of an individual by the number of communities that overlap in that individual, and we prove that the minimum adjacency eigenvalue of the corresponding line graph is not smaller than $-k_{max}$, which is the maximum overlapping depth of the whole network. Based on hypergraphs with preferential attachment, we establish a network model which incorporates overlapping communities with tunable overlapping parameters $k$ and $w$. By comparing with the Hyves social network, we show that our social network model possesses high clustering, assortative mixing, power-law degree distribution and short average path length.

Connected through hyperedge HE-1 (induced by source paper "social community aware content placement in wireless device to device communication networks"):
  with members:
    - "de anonymization of social networks with communities when quantifications meet algorithms"
    - "leveraging social communities for optimizing cellular device to device communications"
    - "cooperative device to device communications with caching"
    - "mean field games for distributed caching in ultra dense small cell networks"
    - "unmanned aerial vehicle with underlaid device to device communications performance and tradeoffs"
    - "context aware small cell networks how social metrics improve wireless resource allocation"
    - "social data offloading in d2d enhanced cellular networks by network formation games"
    - "cooperative game theoretic solution concepts for top k problems"

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)