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 "is less really more why reducing code reuse gadget counts via software debloating doesn t necessarily indicate improved security"):
  Abstract: Nearly all modern software suffers from bloat that negatively impacts its performance and security. To combat this problem, several automated techniques have been proposed to debloat software. A key metric used in many of these works to demonstrate improved security is code reuse gadget count reduction. The use of this metric is based on the prevailing idea that reducing the number of gadgets available in a software package reduces its attack surface and makes mounting a gadget-based code reuse exploit such as return-oriented programming (ROP) more difficult for an attacker. In this paper, we challenge this idea and show through a variety of realistic debloating scenarios the flaws inherent to the gadget count reduction metric. Specifically, we demonstrate that software debloating can achieve high gadget count reduction rates, yet fail to limit an attacker's ability to construct an exploit. Worse yet, in some scenarios high gadget count reduction rates conceal instances in which software debloating makes security worse by introducing new, useful gadgets. To address these issues, we propose a set of four new metrics for measuring security improvements realized through software debloating that are quality-oriented rather than quantity-oriented. We show that these metrics can identify when debloating negatively impacts security and be efficiently calculated using our static binary analysis tool, the Gadget Set Analyzer. Finally, we demonstrate the utility of these metrics in two realistic case studies: iterative debloating and debloater evaluation.

Includes members:
  Member "carve practical security focused software debloating using simple feature set mappings":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "is less really more towards better metrics for measuring security improvements realized through software debloating")
  Member "debloating software through piece wise compilation and loading":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "an empirical study of usages updates and risks of third party libraries in java projects")

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)