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: "international research collaboration novelty conventionality and atypicality in knowledge recombination"
  Abstract: Research articles produced through international collaboration are more highly cited than other work, but are they also more novel? Using measures developed by Uzzi et al. (2013), and replicated by Boyack and Klavans (2014), this article tests for novelty and conventionality in international research collaboration. Scholars have found that coauthored articles are more novel and have suggested that diverse groups have a greater chance of producing creative work. As such, we expected to find that international collaboration tends to produce more novel research. Using data from Web of Science and Scopus in 2005, we failed to show that international collaboration tends to produce more novel articles. In fact, international collaboration appears to produce less novel and more conventional knowledge combinations. Transaction costs and communication barriers to international collaboration may suppress novelty. Higher citations to international work may be explained by an audience effect, where more authors from more countries results in greater access to a larger citing community. The findings are consistent with explanations of growth in international collaboration that posit a social dynamic of preferential attachment based upon reputation.

Connected through hyperedge HE-1 (induced by source paper "academic collaboration rates and citation associations vary substantially between countries and fields"):
  with members:
    - "dimensions a competitor to scopus and the web of science"
    - "google scholar web of science and scopus a systematic comparison of citations in 252 subject categories"
    - "the relationship among research productivity research collaboration and their determinants"
    - "three practical field normalised alternative indicator formulae for research evaluation"
    - "is collaboration among scientists related to the citation impact of papers because their quality increases with collaboration an analysis based on data from f1000prime and normalized citation scores"
    - "national disciplinary and temporal variations in the extent to which articles with more authors have more impact evidence from a geometric field normalised citation indicator"
    - "the journal coverage of web of science and scopus a comparative analysis"
    - "the relationship between the number of authors of a publication its citations and the impact factor of the publishing journal evidence from italy"

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)