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: "shadowguard optimizing the policy and mechanism of shadow stack instrumentation using binary static analysis"
  Abstract: A shadow stack validates on-stack return addresses and prevents arbitrary code execution vulnerabilities due to malicious returns. Several recent works demonstrate that without shadow stack protection, control-flow-integrity -- a related security hardening scheme -- is vulnerable to attacks. Above benefits notwithstanding, shadow stacks have not found mass adoption due to the high overheads they impose. #R##N#In this work, we re-examine the performance viability of shadow stacks as a binary hardening technique. Our work is inspired by the design principle of separating mechanism and policy. Existing research on shadow stacks focus on optimizing the implementation of the shadow stack, which is the mechanism. At a policy level, we define Return Address Safety (RA-Safety) to formally capture the impact of memory writes to return addresses. Based on RA-Safety, we design safe function elision and safe path elision, two novel algorithms to optimize the instrumentation policy for shadow stacks. These two algorithms statically identify functions and control-flow paths that will not overwrite any return address so we can safely elide instrumentation on them. Finally, we compliment above policy improvements with Register frame, Binary function inlining, and Dead register chasing; three new mechanism optimizations. #R##N#We evaluate our new shadow stack implementation ShadowGuard, with SPEC 2017 and show that it reduces the geometric mean overhead from 8% to 2% over an unoptimized shadow stack. We also evaluate several hardened server benchmarks including Apache HTTP Server and Redis, and the results show above techniques significantly reduce the latency and throughput overheads.

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)