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: "system to identify and elide superfluous javascript code for faster webpage loads"
  Abstract: Many websites import large JavaScript (JS) libraries to customize and enhance user experiences. Our data shows that many JS libraries are only partially utilized during a page load, and therefore, contain superfluous code that is never executed. Many top-ranked websites contain up to hundreds of kilobytes of compressed superfluous code and a JS resource on a median page contains 31% superfluous code. Superfluous JS code inflates the page weight, and thereby, the time to download, parse, and compile a JS resource. It is therefore important to monitor the usage and optimize the payload of JS resources to improve Web performance. However, given that the webpage design and functionality could depend on a user's preferences or device, among many other factors, actively loading webpages in controlled environments cannot cover all possible conditions in which webpage content and functionality changes. In this paper, we show that passive measurement techniques, such as real user monitoring systems (RUM), that monitor the performance of real user page loads under different conditions can be leveraged to identify superfluous code. Using a custom man-in-the-middle proxy (similar to a content delivery network's proxy server), we designed a systematic approach for website developers that relies on pages loaded by real users to passively identify superfluous code on JS resources. We then elide any superfluous code from JS resources before subsequent page load requests. Our data shows that eliding superfluous JS code improves the median page load time by 5% and by at least 10% for pages in the long tail. Through results presented in this paper, we motivate for the need for rigorous monitoring of the usage of JS resources under different real world conditions, with the goal to improve Web performance.

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.NI(Networking and Internet Architecture)