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: "acceleration as a service exploiting virtualised gpus for a financial application"
  Abstract: 'How can GPU acceleration be obtained as a service in a cluster?' This question has become increasingly significant due to the inefficiency of installing GPUs on all nodes of a cluster. The research reported in this paper is motivated to address the above question by employing rCUDA (remote CUDA), a framework that facilitates Acceleration-as-a-Service (AaaS), such that the nodes of a cluster can request the acceleration of a set of remote GPUs on demand. The rCUDA framework exploits virtualisation and ensures that multiple nodes can share the same GPU. In this paper we test the feasibility of the rCUDA framework on a real-world application employed in the financial risk industry that can benefit from AaaS in the production setting. The results confirm the feasibility of rCUDA and highlight that rCUDA achieves similar performance compared to CUDA, provides consistent results, and more importantly, allows for a single application to benefit from all the GPUs available in the cluster without loosing efficiency.

Connected through hyperedge HE-1 (induced by source paper "crum checkpoint restart support for cuda s unified memory"):
  with members:
    - "intra node memory safe gpu co scheduling"
    - "vdnn virtualized deep neural networks for scalable memory efficient neural network design"
    - "hard data on soft errors a large scale assessment of real world error rates in gpgpu"
    - "dmtcp transparent checkpointing for cluster computations and the desktop"

Connected through hyperedge HE-2 (induced by source paper "next generation cloud computing new trends and research directions"):
  with members:
    - "occupy the cloud distributed computing for the 99"
    - "on using micro clouds to deliver the fog"
    - "multi tenant virtual gpus for optimising performance of a financial risk application"
    - "big graph search challenges and techniques"
    - "cloudrone micro clouds in the sky"
    - "cloud services brokerage a survey and research roadmap"
    - "big data computing and clouds"
    - "sla oriented resource provisioning for cloud computing challenges architecture and solutions"

Connected through hyperedge HE-3 (induced by source paper "intra node memory safe gpu co scheduling"):
  with members:
    - "a survey of methods for analyzing and improving gpu energy efficiency"
    - "parallel simulations for analysing portfolios of catastrophic event risk"

Connected through hyperedge HE-4 (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"
    - "parallel simulations for analysing portfolios of catastrophic event risk"

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)