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: "sleuth real time attack scenario reconstruction from cots audit data"
  Abstract: We present an approach and system for real-time reconstruction of attack scenarios on an enterprise host. To meet the scalability and real-time needs of the problem, we develop a platform-neutral, main-memory based, dependency graph abstraction of audit-log data. We then present efficient, tag-based techniques for attack detection and reconstruction, including source identification and impact analysis. We also develop methods to reveal the big picture of attacks by construction of compact, visual graphs of attack steps. Our system participated in a red team evaluation organized by DARPA and was able to successfully detect and reconstruct the details of the red team's attacks on hosts running Windows, FreeBSD and Linux.

Connected through hyperedge HE-1 (induced by source paper "unicorn runtime provenance based detector for advanced persistent threats"):
  with members:
    - "poirot aligning attack behavior with kernel audit records for cyber threat hunting"
    - "runtime analysis of whole system provenance"
    - "holmes real time apt detection through correlation of suspicious information flows"
    - "saql a stream based query system for real time abnormal system behavior detection"
    - "how powerful are graph neural networks"
    - "benchmarking crimes an emerging threat in systems security"
    - "frappuccino fault detection through runtime analysis of provenance"
    - "practical whole system provenance capture"

Connected through hyperedge HE-2 (induced by source paper "poirot aligning attack behavior with kernel audit records for cyber threat hunting"):
  with members:
    - "runtime analysis of whole system provenance"
    - "holmes real time apt detection through correlation of suspicious information flows"
    - "propatrol attack investigation via extracted high level tasks"
    - "saql a stream based query system for real time abnormal system behavior detection"
    - "aiql enabling efficient attack investigation from system monitoring data"
    - "behavior query discovery in system generated temporal graphs"
    - "detection of early stage enterprise infection by mining large scale log data"
    - "efficient subgraph matching on billion node graphs"

Connected through hyperedge HE-3 (induced by source paper "a baseline for unsupervised advanced persistent threat detection in system level provenance"):
  with members:
    - "graph based anomaly detection and description a survey"

Connected through hyperedge HE-4 (induced by source paper "holmes real time apt detection through correlation of suspicious information flows"):
  with members:
    - "runtime analysis of whole system provenance"
    - "malicious behavior detection using windows audit logs"

Connected through hyperedge HE-5 (induced by source paper "propatrol attack investigation via extracted high level tasks"):
  with members:
    - "holmes real time apt detection through correlation of suspicious information flows"

Connected through hyperedge HE-6 (induced by source paper "app angiogram revealing contextual information in android app behaviors by api level audit logs"):
  with members:
    - "divide and conquer recovering contextual information of behaviors in android apps around limited quantity audit logs"

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)