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: "deciphering malware s use of tls without decryption"
  Abstract: The use of TLS by malware poses new challenges to network threat detection because traditional pattern-matching techniques can no longer be applied to its messages. However, TLS also introduces a complex set of observable data features that allow many inferences to be made about both the client and the server. We show that these features can be used to detect and understand malware communication, while at the same time preserving the privacy of benign uses of encryption. These data features also allow for accurate malware family attribution of network communication, even when restricted to a single, encrypted flow. #R##N#To demonstrate this, we performed a detailed study of how TLS is used by malware and enterprise applications. We provide a general analysis on millions of TLS encrypted flows, and a targeted study on 18 malware families composed of thousands of unique malware samples and ten-of-thousands of malicious TLS flows. Importantly, we identify and accommodate the bias introduced by the use of a malware sandbox. The performance of a malware classifier is correlated with a malware family's use of TLS, i.e., malware families that actively evolve their use of cryptography are more difficult to classify. #R##N#We conclude that malware's usage of TLS is distinct from benign usage in an enterprise setting, and that these differences can be effectively used in rules and machine learning classifiers.

Connected through hyperedge HE-1 (induced by source paper "malpaca malware packet sequence clustering and analysis"):
  with members:
    - "android malware clustering through malicious payload mining"
    - "monet a user oriented behavior based malware variants detection system for android"
    - "technical aspects of cyber kill chain"

Connected through hyperedge HE-2 (induced by source paper "beyond labeling intuitive capability assessment of malware using network behavioral profiles"):
  with members:
    - "android malware clustering through malicious payload mining"
    - "monet a user oriented behavior based malware variants detection system for android"

Connected through hyperedge HE-3 (induced by source paper "deep learning for malicious flow detection"):
  with members:
    - "synthetic minority over sampling technique smote for predicting software build outcomes"

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)