human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "silhouette efficient intra address space isolation for protected shadow stacks on embedded systems"):
  Abstract: Embedded systems are increasingly deployed in devices that can have physical consequences if compromised by an attacker - including automobile control systems, smart locks, drones, and implantable medical devices. Due to resource and execution-time constraints, C is the primary programming language for writing both operating systems and bare-metal applications on these devices. Unfortunately, C is neither memory safe nor type safe. #R##N#In this paper, we present an efficient intra-address space isolation technique for embedded ARM processors that leverages unprivileged store instructions. Using a compiler transformation, dubbed store promotion, which transforms regular stores into unprivileged equivalents, we can restrict a subset of the program's memory accesses to unprivileged memory while simultaneously allowing security instrumentation to access security-critical data structures (e.g., a shadow stack) - all without the need for expensive context switching. #R##N#Using store promotion, we built Silhouette: a software defense that mitigates control-flow hijacking attacks using a combination of hardware configuration, runtime instrumentation, and code transformation. Silhouette enforces Control-Flow Integrity and provides an incorruptible shadow stack for return addresses. We implemented Silhouette on an ARMv7-M board and our evaluation shows that Silhouette incurs an arithmetic mean of 9.23% performance overhead and 16.93% code size overhead. Furthermore, we built Silhouette-Invert, an alternative implementation of Silhouette, which incurs just 2.54% performance overhead and 5.40% code size overhead, at the cost of a minor hardware change.

Includes members:
  Member "control flow integrity precision security and performance":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "methodologies for quantifying re randomization security and timing under jit rop")
  Member "beebs open benchmarks for energy measurements on embedded platforms":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "silhouette efficient protected shadow stacks on embedded systems")

Question: Please tell me which class the source paper 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)