human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "malpaca malware packet sequence clustering and analysis"):
  Abstract: Malware family characterization is a challenging problem because ground-truth labels are not known. Anti-virus solutions provide labels for malware samples based on their static analysis. However, these labels are known to be inconsistent, causing the evaluation of analysis methods to depend on unreliable ground truth labels. These analysis methods are often black-boxes that make it impossible to verify the assigned family labels. To support malware analysts, we propose a whitebox method named MalPaCA to cluster malware's attacking capabilities reflected in their network traffic. We use sequential features to model temporal behavior. We also propose an intuitive, visualization-based cluster evaluation method to solve interpretability issues. The results show that clustering malware's attacking capabilities provides a more intimate profile of a family's behavior. The identified clusters capture various attacking capabilities, such as port scans and reuse of C\&C servers. We discover a number of discrepancies between behavioral clusters and traditional malware family designations. In these cases, behavior within a family group was so varied that many supposedly related malwares had more in common with malware from other families than within their family designation. We also show that sequential features are better suited for modeling temporal behavior than statistical aggregates.

Includes members:
  Member "android malware clustering through malicious payload mining":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "droidetec android malware detection and malicious code localization through deep learning")
  Member "monet a user oriented behavior based malware variants detection system for android":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "beyond labeling intuitive capability assessment of malware using network behavioral profiles")
  Member "deciphering malware s use of tls without decryption":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "beyond labeling intuitive capability assessment of malware using network behavioral profiles")
  Member "technical aspects of cyber kill chain":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "unicorn runtime provenance based detector for advanced persistent threats")

Question: Please tell me which class the source paper 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)