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: "beyond labeling intuitive capability assessment of malware using network behavioral profiles"
  Abstract: Malicious software is still a leading threat in cybersecurity. Anti-Virus (AV) companies are pivotal in understanding and assigning labels to new malware samples. Currently, these labels are the sole source of ground truth information available to the security community to evaluate malware analysis methods. However, their adopted naming conventions are known to be inconsistent and unverifiable. The labels are also black box since they do not represent the capabilities of malware. We believe that we need a white box way to determine the capabilities of malware based on their behavior, rather than black box family labels. The current state of the art in malware capability assessment contains largely manual approaches.  We propose a novel method called MalPaCA, which for a large part automates capability assessment by clustering temporal behavior observed in malware's network traces. MalPaCA uses network traces since most malware uses the Internet to carry out its objectives. In doing so, we build behavioral profiles of malware families that are significantly more descriptive than their black box family names. We also propose an intuitive, visualization-based cluster evaluation method for the obtained clusters. We evaluate MalPaCA on 1.1k malware samples collected in the wild. MalPaCA shows promising results: (i) It correctly discovers capabilities, such as port scans and reuse of Command and Control servers; (ii) It discovers a number of discrepancies between behavioral clusters and traditional malware family designations; and (iii) It demonstrates the effectiveness of clustering unlabeled network traces using temporal features by producing a false positive rate of mere 8%.

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)