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: "network maps of technology fields a comparative analysis of relatedness measures"
  Abstract: Network maps of technology fields extracted from patent databases are useful to aid in technology forecasting and road mapping. Constructing such a network requires a measure of the relatedness between pairs of technology fields. Despite the existence of various relatedness measures in the literature, it is unclear how to consistently assess and compare them, and which ones to select for constructing technology network maps. This ambiguity has limited the use of technology network maps for technology forecasting and roadmap analyses. To address this challenge, here we propose a strategy to evaluate alternative relatedness measures and identify the superior ones by comparing the structure properties of resulting technology networks. Using United States patent data, we execute the strategy through a comparative analysis of twelve relatedness measures, which quantify inter-field knowledge input similarity, field-crossing diversification likelihood or frequency of innovation agents, and co-occurrences of technology classes in the same patents. Our comparative analyses suggest two superior relatedness measures, normalized co-reference and inventor diversification likelihood, for constructing technology network maps.

Connected through hyperedge HE-1 (induced by source paper "filtered patent maps for predicting diversification paths of inventors and organizations"):
  with members:
    - "measuring technological distance for patent mapping"

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)