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: "geodblp geo tagging dblp for mining the sociology of computer science"
  Abstract: Many collective human activities have been shown to exhibit universal patterns. However, the possibility of universal patterns across timing events of researcher migration has barely been explored at global scale. Here, we show that timing events of migration within different countries exhibit remarkable similarities. Specifically, we look at the distribution governing the data of researcher migration inferred from the web. Compiling the data in itself represents a significant advance in the field of quantitative analysis of migration patterns. Official and commercial records are often access restricted, incompatible between countries, and especially not registered across researchers. Instead, we introduce GeoDBLP where we propagate geographical seed locations retrieved from the web across the DBLP database of 1,080,958 authors and 1,894,758 papers. But perhaps more important is that we are able to find statistical patterns and create models that explain the migration of researchers. For instance, we show that the science job market can be treated as a Poisson process with individual propensities to migrate following a log-normal distribution over the researcher's career stage. That is, although jobs enter the market constantly, researchers are generally not "memoryless" but have to care greatly about their next move. The propensity to make k>1 migrations, however, follows a gamma distribution suggesting that migration at later career stages is "memoryless". This aligns well but actually goes beyond scientometric models typically postulated based on small case studies. On a very large, transnational scale, we establish the first general regularities that should have major implications on strategies for education and research worldwide.

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)