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: "frappuccino fault detection through runtime analysis of provenance"
  Abstract: We present FRAPpuccino (or FRAP), a provenance-based fault detection mechanism for Platform as a Service (PaaS) users, who run many instances of an application on a large cluster of machines. FRAP models, records, and analyzes the behavior of an application and its impact on the system as a directed acyclic provenance graph. It assumes that most instances behave normally and uses their behavior to construct a model of legitimate behavior. Given a model of legitimate behavior, FRAP uses a dynamic sliding window algorithm to compare a new instance's execution to that of the model. Any instance that does not conform to the model is identified as an anomaly. We present the FRAP prototype and experimental results showing that it can accurately detect application anomalies.

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"
    - "sleuth real time attack scenario reconstruction from cots audit data"
    - "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"
    - "practical whole system provenance capture"

Connected through hyperedge HE-2 (induced by source paper "decision provenance harnessing data flow for accountable systems"):
  with members:
    - "datasheets for datasets"
    - "camflow managed data sharing for cloud services"
    - "practical whole system provenance capture"
    - "internet of things iot a vision architectural elements and future directions"

Connected through hyperedge HE-3 (induced by source paper "provmark a provenance expressiveness benchmarking system"):
  with members:
    - "runtime analysis of whole system provenance"
    - "camflow managed data sharing for cloud services"
    - "practical whole system provenance capture"

Connected through hyperedge HE-4 (induced by source paper "runtime analysis of whole system provenance"):
  with members:
    - "provenance based intrusion detection opportunities and challenges"
    - "saql a stream based query system for real time abnormal system behavior detection"
    - "struc2vec learning node representations from structural identity"
    - "camflow managed data sharing for cloud services"
    - "practical whole system provenance capture"
    - "high throughput ingest of provenance records into accumulo"
    - "attack analysis results for adversarial engagement 1 of the darpa transparent computing program"

Connected through hyperedge HE-5 (induced by source paper "provenance based intrusion detection opportunities and challenges"):
  with members:
    - "graph based anomaly detection and description a survey"

Connected through hyperedge HE-6 (induced by source paper "practical whole system provenance capture"):
  with members:
    - "camflow managed data sharing for cloud services"
    - "high throughput ingest of provenance records into accumulo"
    - "retrofitting applications with provenance based security monitoring"

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.SY(Systems and Control)