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: "sip shaker software integrity protection composition"
  Abstract: Man-At-The-End (MATE) attackers are almighty adversaries against whom there exists no silver-bullet countermeasure. To raise the bar, a wide range of protection measures were proposed in the literature each of which adds resilience against certain attacks on certain digital assets of a program. Intuitively, composing a set of protections (rather than applying just one of them) can mitigate a wider range of attacks and hence offer a higher level of security. Despite the potential benefits, very limited research has been done on the composition of protections. Naive compositions could lead to conflicts which, in turn, limit the application of protections, raise false alarms, and worse yet, yield corrupted binaries. More importantly, inadequate compositions of such protections are not tailored for the program at hand and thus the offered security and performance are sub-optimal. In this paper, we first lay out a set of generic constraints for a conflict-free composition of protections. Then, we develop a composition framework based on a defense graph in which nodes and edges capture protections, their relations, and constraints. The conflicts problem together with optimization requirements are then translated into a set of integer constraints. We then use Integer Linear Programming (ILP) to handle conflicts while optimizing for a higher security and lower overhead. To measure the overhead, we use a set of real-world programs (MiBench dataset and open source games). Our evaluation results indicate that our composition framework reduces the overhead by $\approx$ 39% while maximizing the coverage. Moreover, our approach yields a 5-fold decrease in overhead compared to state-of-the-art heuristics.

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)