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: "hard data on soft errors a large scale assessment of real world error rates in gpgpu"
  Abstract: Graphics processing units (GPUs) are gaining widespread use in computational chemistry and other scientific simulation contexts because of their huge performance advantages relative to conventional CPUs. However, the reliability of GPUs in error-intolerant applications is largely unproven. In particular, a lack of error checking and correcting (ECC) capability in the memory subsystems of graphics cards has been cited as a hindrance to the acceptance of GPUs as high-performance coprocessors, but the impact of this design has not been previously quantified. #R##N#In this article we present MemtestG80, our software for assessing memory error rates on NVIDIA G80 and GT200-architecture-based graphics cards. Furthermore, we present the results of a large-scale assessment of GPU error rate, conducted by running MemtestG80 on over 20,000 hosts on the Folding@home distributed computing network. Our control experiments on consumer-grade and dedicated-GPGPU hardware in a controlled environment found no errors. However, our survey over cards on Folding@home finds that, in their installed environments, two-thirds of tested GPUs exhibit a detectable, pattern-sensitive rate of memory soft errors. We demonstrate that these errors persist after controlling for overclocking and environmental proxies for temperature, but depend strongly on board architecture.

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"
    - "acceleration as a service exploiting virtualised gpus for a financial application"
    - "dmtcp transparent checkpointing for cluster computations and the desktop"

Connected through hyperedge HE-2 (induced by source paper "exploiting data representation for fault tolerance"):
  with members:
    - "evaluating the impact of sdc on the gmres iterative solver"

Connected through hyperedge HE-3 (induced by source paper "computing linear transformations with unreliable components"):
  with members:
    - "speeding up distributed machine learning using codes"
    - "short dot computing large linear transforms distributedly using coded short dot products"
    - "information friction and its implications on minimum energy required for communication"
    - "density evolution and functional threshold for the noisy min sum decoder"
    - "density evolution for min sum decoding of ldpc codes under unreliable message storage"
    - "energy consumption of vlsi decoders"
    - "coding with encoding uncertainty"
    - "spatially coupled ensembles universally achieve capacity under belief propagation"

Connected through hyperedge HE-4 (induced by source paper "evaluating the impact of sdc on the gmres iterative solver"):
  with members:
    - "massively parallel solvers for elliptic pdes in numerical weather and climate prediction"

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.AR(Hardware Architecture)