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: "parallel simulations for analysing portfolios of catastrophic event risk"
  Abstract: At the heart of the analytical pipeline of a modern quantitative insurance/reinsurance company is a stochastic simulation technique for portfolio risk analysis and pricing process referred to as Aggregate Analysis. Support for the computation of risk measures including Probable Maximum Loss (PML) and the Tail Value at Risk (TVAR) for a variety of types of complex property catastrophe insurance contracts including Cat eXcess of Loss (XL), or Per-Occurrence XL, and Aggregate XL, and contracts that combine these measures is obtained in Aggregate Analysis. #R##N#In this paper, we explore parallel methods for aggregate risk analysis. A parallel aggregate risk analysis algorithm and an engine based on the algorithm is proposed. This engine is implemented in C and OpenMP for multi-core CPUs and in C and CUDA for many-core GPUs. Performance analysis of the algorithm indicates that GPUs offer an alternative HPC solution for aggregate risk analysis that is cost effective. The optimised algorithm on the GPU performs a 1 million trial aggregate simulation with 1000 catastrophic events per trial on a typical exposure set and contract structure in just over 20 seconds which is approximately 15x times faster than the sequential counterpart. This can sufficiently support the real-time pricing scenario in which an underwriter analyses different contractual terms and pricing while discussing a deal with a client over the phone.

Connected through hyperedge HE-1 (induced by source paper "cloud benchmarking for maximising performance of scientific applications"):
  with members:
    - "cloud benchmarking for performance"
    - "are clouds ready to accelerate ad hoc financial simulations"
    - "on a catalogue of metrics for evaluating commercial cloud services"

Connected through hyperedge HE-2 (induced by source paper "intra node memory safe gpu co scheduling"):
  with members:
    - "acceleration as a service exploiting virtualised gpus for a financial application"
    - "a survey of methods for analyzing and improving gpu energy efficiency"

Connected through hyperedge HE-3 (induced by source paper "doclite a docker based lightweight cloud benchmarking tool"):
  with members:
    - "cloud services brokerage a survey and research roadmap"
    - "analysis of docker security"
    - "an introduction to docker for reproducible research with examples from the r environment"
    - "academic cloud computing research five pitfalls and five opportunities"
    - "cloud benchmarking for performance"
    - "on a catalogue of metrics for evaluating commercial cloud services"

Connected through hyperedge HE-4 (induced by source paper "container based cloud virtual machine benchmarking"):
  with members:
    - "analysis of docker security"
    - "an introduction to docker for reproducible research with examples from the r environment"
    - "optimal deployment of geographically distributed workflow engines on the cloud"
    - "cloud benchmarking for performance"
    - "location location location data intensive distributed computing in the cloud"
    - "on a catalogue of metrics for evaluating commercial cloud services"

Connected through hyperedge HE-5 (induced by source paper "multi tenant virtual gpus for optimising performance of a financial risk application"):
  with members:
    - "the gpu vs phi debate risk analytics using many core computing"
    - "acceleration as a service exploiting virtualised gpus for a financial application"

Connected through hyperedge HE-6 (induced by source paper "acceleration as a service exploiting virtualised gpus for a financial application"):
  with members:
    - "the gpu vs phi debate risk analytics using many core computing"

Connected through hyperedge HE-7 (induced by source paper "cloud benchmarking for performance"):
  with members:
    - "on a catalogue of metrics for evaluating commercial cloud services"

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.DC(Distributed, Parallel, and Cluster Computing)