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: "camflow managed data sharing for cloud services"
  Abstract: A model of cloud services is emerging whereby a few trusted providers manage the underlying hardware and communications whereas many companies build on this infrastructure to offer higher level, cloud-hosted PaaS services and/or SaaS applications. From the start, strong isolation between cloud tenants was seen to be of paramount importance, provided first by virtual machines (VM) and later by containers, which share the operating system (OS) kernel. Increasingly it is the case that  applications  also require facilities to effect   isolation and protection  of data managed by those applications. They also require  flexible data sharing  with other applications, often across the traditional cloud-isolation boundaries; for example, when government, consisting of different departments, provides services to its citizens through a common platform. These concerns relate to the management of data. Traditional access control is application and principal/role specific, applied at policy enforcement points, after which there is no subsequent control over where data flows;a crucial issue once data has left its owner’s control by cloud-hosted applications andwithin cloud-services. Information Flow Control (IFC), in addition, offers system-wide, end-to-end, flow control based on the properties of the data. We discuss the potential of cloud-deployed IFC for  enforcing  owners’ data flow policy with regard to protection and sharing, aswell as safeguarding against malicious or buggy software. In addition, the audit log associated with IFC provides transparency and offers system-wide visibility over data flows. This helps those responsible to meet their data management obligations, providing evidence of compliance, and aids in the identification ofpolicy errors and misconfigurations. We present our IFC model and describe and evaluate our IFC architecture and implementation (CamFlow). This comprises an OS level implementation of IFC with support for application management, together with an IFC-enabled middleware.

Connected through hyperedge HE-1 (induced by source paper "decision provenance harnessing data flow for accountable systems"):
  with members:
    - "datasheets for datasets"
    - "frappuccino fault detection through runtime analysis of provenance"
    - "practical whole system provenance capture"
    - "internet of things iot a vision architectural elements and future directions"

Connected through hyperedge HE-2 (induced by source paper "provmark a provenance expressiveness benchmarking system"):
  with members:
    - "runtime analysis of whole system provenance"
    - "frappuccino fault detection through runtime analysis of provenance"
    - "practical whole system provenance capture"

Connected through hyperedge HE-3 (induced by source paper "towards a computer interpretable actionable formal model to encode data governance rules"):
  with members:
    - "shai enforcing data specific policies with near zero runtime overhead"
    - "mind my value a decentralized infrastructure for fair and trusted iot data trading"

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"
    - "frappuccino fault detection through runtime analysis of provenance"
    - "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 "practical whole system provenance capture"):
  with members:
    - "frappuccino fault detection through runtime analysis of provenance"
    - "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.CR(Cryptography and Security)