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: "analyzing machine learning workloads using a detailed gpu simulator"
  Abstract: Most deep neural networks deployed today are trained using GPUs via high-level frameworks such as TensorFlow and PyTorch. This paper describes changes we made to the GPGPU-Sim simulator to enable it to run PyTorch by running PTX kernels included in NVIDIA's cuDNN library. We use the resulting modified simulator, which has been made available publicly with this paper, to study some simple deep learning workloads. With our changes to GPGPU-Sim's functional simulation model, we find GPGPU-Sim performance model running a cuDNN enabled implementation of LeNet for MNIST reports results within 30% of real hardware. Using GPGPU-Sim's AerialVision performance analysis tool we observe that cuDNN API calls contain many varying phases and appear to include potentially inefficient microarchitecture behaviour such as DRAM partition bank camping, at least when executed on GPGPU-Sim's current performance model.

Connected through hyperedge HE-1 (induced by source paper "performance aware convolutional neural network channel pruning for embedded gpus"):
  with members:
    - "tbd benchmarking and analyzing deep neural network training"
    - "squeeze and excitation networks"
    - "towards end to end speech recognition with deep convolutional neural networks"
    - "channel pruning for accelerating very deep neural networks"
    - "densely connected convolutional networks"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "autotuning opencl workgroup size for stencil patterns"
    - "learning both weights and connections for efficient neural networks"

Connected through hyperedge HE-2 (induced by source paper "optimizing gpu cache policies for mi workloads"):
  with members:
    - "scnn an accelerator for compressed sparse convolutional neural networks"
    - "massive exploration of neural machine translation architectures"
    - "maximizing cnn accelerator efficiency through resource partitioning"
    - "eie efficient inference engine on compressed deep neural network"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-3 (induced by source paper "mirovia a benchmarking suite for modern heterogeneous computing"):
  with members:
    - "tango a deep neural network benchmark suite for various accelerators"
    - "nvidia tensor core programmability performance precision"
    - "tbd benchmarking and analyzing deep neural network training"
    - "in datacenter performance analysis of a tensor processing unit"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-4 (induced by source paper "modeling deep learning accelerator enabled gpus"):
  with members:
    - "exploring modern gpu memory system design challenges through accurate modeling"
    - "nvidia tensor core programmability performance precision"
    - "dissecting the nvidia volta gpu architecture via microbenchmarking"
    - "in datacenter performance analysis of a tensor processing unit"
    - "tensorflow a system for large scale machine learning"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "very deep convolutional networks for large scale image recognition"
    - "cudnn efficient primitives for deep learning"

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)