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: "effect of pipelining and multiplexing in estimating http 2 0 web object sizes"
  Abstract: HTTP response size is a well-known side channel attack. With the deployment of HTTP/2.0, response size estimation attacks are generally dismissed with the argument that pipelining and response multiplexing prevent eavesdroppers from finding out response sizes. Yet the impact that pipelining and response multiplexing actually have in estimating HTTP response sizes has not been adequately investigated. In this paper we set out to help understand the effect of pipelining and response multiplexing in estimating the size of web objects on the Internet. We conduct an experiment that collects HTTP response sizes and TLS record sizes from 10k popular web sites. We gather evidence on and discuss reasons for the limited amount of pipelining and response multiplexing used on the Internet today: only 29% of the HTTP2 web objects we observe are pipelined and only 5% multiplexed. We also provide worst case results under different attack assumptions and show how effective a simple model for estimating response sizes from TLS record sizes can be. Our conclusion is that pipelining and especially response multiplexing can yield, as expected, a perceivable increase in relative object size estimation error yet the limited extent of multiplexing observed on the Internet today and the relative simplicity of attacks to the current pipelining mechanisms hinder their ability to help prevent web object size estimation.

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.CR(Cryptography and Security)