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 "graalf supporting graphical analysis of audit logs for forensics"):
  Abstract: System-call level audit logs often play a critical role in computer forensics. They capture low-level interactions between programs and users in much detail, making them a rich source of insight on malicious user activity. However, using these logs to discover and understand malicious activities from a typical computer that produces more than 3GB of logs daily is both compute and time intensive. We introduce a graphical system for efficient loading, storing, processing, querying, and displaying system events to support computer forensics called GrAALF. In comparison to other related systems such as AIQL [13] and SAQL [12], GrAALF offers the flexibility of multiple backend storage solutions, easy-to-use and intuitive querying of logs, and the ability to trace back longer sequences of system events in (near) real-time to help identify and isolate attacks. Equally important, both AIQL and SAQL are not available for public use, whereas GrAALF is open-source. GrAALF offers the choice of compactly storing the logs in main memory, in a relational database system, in a hybrid main memory database system, and a graph-based database. We compare the responsiveness of each of these options, using multiple very large system-call log files. Next, in multiple real-world attack scenarios, we demonstrate the efficacy and usefulness of GrAALF in identifying the attack and discovering its provenance. Consequently, GrAALF offers a robust solution for analysis of audit logs to support computer forensics

Includes members:
  Member "saql a stream based query system for real time abnormal system behavior detection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "unicorn runtime provenance based detector for advanced persistent threats")
  Member "aiql enabling efficient attack investigation from system monitoring data":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "querying streaming system monitoring data for enterprise system anomaly detection")

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)