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: "empirical review of automated analysis tools on 47 587 ethereum smart contracts"
  Abstract: Over the last few years, there has been substantial research on automated analysis, testing, and debugging of Ethereum smart contracts. However, it is not trivial to compare and reproduce that research. To address this, we present an empirical evaluation of 9 state-of-the-art automated analysis tools using two new datasets: i) a dataset of 69 annotated vulnerable smart contracts that can be used to evaluate the precision of analysis tools; and ii) a dataset with all the smart contracts in the Ethereum Blockchain that have Solidity source code available on Etherscan (a total of 47,518 contracts). The datasets are part of SmartBugs, a new extendable execution framework that we created to facilitate the integration and comparison between multiple analysis tools and the analysis of Ethereum smart contracts. We used SmartBugs to execute the 9 automated analysis tools on the two datasets. In total, we ran 428,337 analyses that took approximately 564 days and 3 hours, being the largest experimental setup to date both in the number of tools and in execution time. We found that only 42% of the vulnerabilities from our annotated dataset are detected by all the tools, with the tool Mythril having the higher accuracy (27%). When considering the largest dataset, we observed that 97% of contracts are tagged as vulnerable, thus suggesting a considerable number of false positives. Indeed, only a small number of vulnerabilities (and of only two categories) were detected simultaneously by four or more tools.

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.SE(Software Engineering)