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: "input aware auto tuning of compute bound hpc kernels"
  Abstract: Efficient implementations of HPC applications for parallel architectures generally rely on external software packages (e.g., BLAS, LAPACK, CUDNN). While these libraries provide highly optimized routines for certain characteristics of inputs (e.g., square matrices), they generally do not retain optimal performance across the wide range of problems encountered in practice. In this paper, we present an input-aware auto-tuning framework for matrix multiplications and convolutions, ISAAC, which uses predictive modeling techniques to drive highly parameterized PTX code templates towards not only hardware-, but also application-specific kernels. Numerical experiments on the NVIDIA Maxwell and Pascal architectures show up to 3x performance gains over both cuBLAS and cuDNN after only a few hours of auto-tuning.

Connected through hyperedge HE-1 (induced by source paper "optimizing streaming parallelism on heterogeneous many core architectures"):
  with members:
    - "machine learning in compiler optimisation"
    - "hstream a directive based language extension for heterogeneous stream computing"
    - "improving spark application throughput via memory aware task co location a mixture of experts approach"
    - "evaluating the performance impact of multiple streams on the mic based heterogeneous platform"
    - "streaming applications on heterogeneous platforms"

Connected through hyperedge HE-2 (induced by source paper "runtime concurrency control and operation scheduling for high performance neural network training"):
  with members:
    - "ray a distributed framework for emerging ai applications"
    - "scaling deep learning on gpu and knights landing clusters"
    - "device placement optimization with reinforcement learning"
    - "tensorflow agents efficient batched reinforcement learning in tensorflow"
    - "optimizing memory efficiency for deep convolutional neural networks on gpus"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "deep residual learning for image recognition"
    - "mxnet a flexible and efficient machine learning library for heterogeneous distributed systems"

Connected through hyperedge HE-3 (induced by source paper "clblast a tuned opencl blas library"):
  with members:
    - "sobol tensor trains for global sensitivity analysis"
    - "mixed precision training"
    - "parallel multi channel convolution using general matrix multiplication"
    - "machine learning based auto tuning for enhanced opencl performance portability"
    - "cltune a generic auto tuner for opencl kernels"
    - "efficient convolutional neural networks for pixelwise classification on heterogeneous hardware systems"
    - "cudnn efficient primitives for deep learning"

Connected through hyperedge HE-4 (induced by source paper "a model driven approach for a new generation of adaptive libraries"):
  with members:
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "machine learning based auto tuning for enhanced opencl performance portability"
    - "cltune a generic auto tuner for opencl kernels"
    - "going deeper with convolutions"

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)