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: "detecting malware with information complexity"
  Abstract: This work focuses on a specific front of the malware detection arms-race, namely the detection of persistent, disk-resident malware. We exploit normalised compression distance (NCD), an information theoretic measure, applied directly to binaries. Given a zoo of labelled malware and benign-ware, we ask whether a suspect program is more similar to our malware or to our benignware. Our approach classifies malware with 97.1% accuracy and a false positive rate of 3%. We achieve our results with off-theshelf compressors and a standard machine learning classifier and without any specialised knowledge. An end-user need only collect a zoo of malware and benign-ware and then can immediately apply our techniques. We apply statistical rigour to our experiments and our selection of data. We demonstrate that accuracy can be optimised by combining NCD with the compressibility rates of the executables. We demonstrate that malware reported within a more narrow time frame of a few days is more homogenous than malware reported over a longer one of two years but that our method still classifies the latter with 95.2% accuracy and a 5% false positive rate. Due to the use of compression, the time and computation cost of our method is non-trivial. We show that simple approximation techniques can improve the time complexity of our approach by up to 63%. We compare our results to the results of applying the 59 antimalware programs used on the VirusTotal web site to our malware. Our approach does better than any single one of them as well as the 59 used collectively.

Connected through hyperedge HE-1 (induced by source paper "lempel ziv jaccard distance an effective alternative to ssdeep and sdhash"):
  with members:
    - "on normalized compression distance and large malware"
    - "the similarity metric"

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)