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: "software ethology an accurate and resilient semantic binary analysis framework"
  Abstract: When reverse engineering a binary, the analyst must first understand the semantics of the binary's functions through either manual or automatic analysis. Manual semantic analysis is time-consuming, because abstractions provided by high level languages, such as type information, variable scope, or comments are lost, and past analyses cannot apply to the current analysis task. Existing automated binary analysis tools currently suffer from low accuracy in determining semantic function identification in the presence of diverse compilation environments. #R##N#We introduce Software Ethology, a binary analysis approach for determining the semantic similarity of functions. Software Ethology abstracts semantic behavior as classification vectors of program state changes resulting from a function executing with a specified input state, and uses these vectors as a unique fingerprint for identification. All existing semantic identifiers determine function similarity via code measurements, and suffer from high inaccuracy when classifying functions from compilation environments different from their ground truth source. Since Software Ethology does not rely on code measurements, its accuracy is resilient to changes in compiler, compiler version, optimization level, or even different source implementing equivalent functionality. #R##N#Tinbergen, our prototype Software Ethology implementation, leverages a virtual execution environment and a fuzzer to generate the classification vectors. In evaluating Tinbergen's feasibility as a semantic function identifier by identifying functions in coreutils-8.30, we achieve a high .805 average accuracy. Compared to the state-of-the-art, Tinbergen is 1.5 orders of magnitude faster when training, 50% faster in answering queries, and, when identifying functions in binaries generated from differing compilation environments, is 30%-61% more accurate.

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)