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: "high throughput ingest of provenance records into accumulo"
  Abstract: Whole-system data provenance provides deep insight into the processing of data on a system, including detecting data integrity attacks. The downside to systems that collect whole-system data provenance is the sheer volume of data that is generated under many heavy workloads. In order to make provenance metadata useful, it must be stored somewhere where it can be queried. This problem becomes even more challenging when considering a network of provenance-aware machines all collecting this metadata. In this paper, we investigate the use of D4M and Accumulo to support high-throughput data ingest of whole-system provenance data. We find that we are able to ingest 3,970 graph components per second. Centrally storing the provenance metadata allows us to build systems that can detect and respond to data integrity attacks that are captured by the provenance system.

Connected through hyperedge HE-1 (induced by source paper "large scale organization and inference of an imagery dataset for public safety"):
  with members:
    - "interactive supercomputing on 40 000 cores for machine learning and data analysis"
    - "youtube 8m a large scale video classification benchmark"
    - "lustre hadoop accumulo"
    - "incentivizing high quality crowdwork"
    - "imagenet large scale visual recognition challenge"
    - "d4m 2 0 schema a general purpose high performance schema for the accumulo database"

Connected through hyperedge HE-2 (induced by source paper "viewpoint personal data and the internet of things it is time to care about digital provenance"):
  with members:
    - "runtime analysis of whole system provenance"
    - "provabs model policy and tooling for abstracting prov graphs"
    - "provenance views for module privacy"

Connected through hyperedge HE-3 (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"
    - "frappuccino fault detection through runtime analysis of provenance"
    - "practical whole system provenance capture"
    - "attack analysis results for adversarial engagement 1 of the darpa transparent computing program"

Connected through hyperedge HE-4 (induced by source paper "practical whole system provenance capture"):
  with members:
    - "camflow managed data sharing for cloud services"
    - "frappuccino fault detection through runtime analysis of provenance"
    - "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.DB(Databases)