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: "anatomy of high performance deep learning convolutions on simd architectures"
  Abstract: Convolution layers are prevalent in many classes of deep neural networks, including Convolutional Neural Networks (CNNs) which provide state-of-the-art results for tasks like image recognition, neural machine translation and speech recognition. The computationally expensive nature of a convolution operation has led to the proliferation of implementations including matrix-matrix multiplication formulation, and direct convolution primarily targeting GPUs. In this paper, we introduce direct convolution kernels for x86 architectures, in particular for Xeon and XeonPhi systems, which are implemented via a dynamic compilation approach. Our JIT-based implementation shows close to theoretical peak performance, depending on the setting and the CPU architecture at hand. We additionally demonstrate how these JIT-optimized kernels can be integrated into a lightweight multi-node graph execution model. This illustrates that single- and multi-node runs yield high efficiencies and high image-throughputs when executing state-of-the-art image recognition tasks on CPUs.

Connected through hyperedge HE-1 (induced by source paper "high performance depthwise and pointwise convolutions on mobile devices"):
  with members:
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "tvm an automated end to end optimizing compiler for deep learning"
    - "diagonalwise refactorization an efficient training method for depthwise convolutions"
    - "high performance zero memory overhead direct convolutions"
    - "mnasnet platform aware neural architecture search for mobile"
    - "mec memory efficient convolution for deep neural network"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "xception deep learning with depthwise separable convolutions"

Connected through hyperedge HE-2 (induced by source paper "the indirect convolution algorithm"):
  with members:
    - "pay less attention with lightweight and dynamic convolutions"
    - "panoptic feature pyramid networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "yolov3 an incremental improvement"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "high performance zero memory overhead direct convolutions"
    - "pooling pyramid network for object detection"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"

Connected through hyperedge HE-3 (induced by source paper "a study of bfloat16 for deep learning training"):
  with members:
    - "deep learning recommendation model for personalization and recommendation systems"
    - "high performance monte carlo simulation of ising model on tpu clusters"
    - "mixed precision training of convolutional neural networks using integer operations"
    - "automatic full compilation of julia programs and ml models to cloud tpus"
    - "deep cross network for ad click predictions"
    - "mixed precision training"
    - "ternary neural networks with fine grained quantization"
    - "flexpoint an adaptive numerical format for efficient training of deep neural networks"

Connected through hyperedge HE-4 (induced by source paper "high performance deep learning via a single building block"):
  with members:
    - "stripe tensor compilation via the nested polyhedral model"
    - "large batch training for lstm and beyond"
    - "nvidia tensor core programmability performance precision"
    - "high accuracy low precision training"
    - "glow graph lowering compiler techniques for neural networks"
    - "tvm an automated end to end optimizing compiler for deep learning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "tensor comprehensions framework agnostic high performance machine learning abstractions"

Connected through hyperedge HE-5 (induced by source paper "sparsetrain leveraging dynamic sparsity in training dnns on general purpose simd processors"):
  with members:
    - "fixup initialization residual learning without normalization"
    - "efficient sparse winograd convolutional neural networks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "meprop sparsified back propagation for accelerated deep learning with reduced overfitting"
    - "comparison of batch normalization and weight normalization algorithms for the large scale image classification"
    - "compressing dma engine leveraging activation sparsity for training deep neural networks"
    - "sparce sparsity aware general purpose core extensions to accelerate deep neural networks"
    - "learning structured sparsity in deep neural networks"

Connected through hyperedge HE-6 (induced by source paper "optimizing cnn model inference on cpus"):
  with members:
    - "tvm end to end optimization stack for deep learning"
    - "glow graph lowering compiler techniques for neural networks"
    - "learning to optimize tensor programs"
    - "fft convolutions are faster than winograd on modern cpus here is why"
    - "intel ngraph an intermediate representation compiler and executor for deep learning"
    - "tensor comprehensions framework agnostic high performance machine learning abstractions"
    - "scheduling computation graphs of deep learning models on manycore cpus"
    - "in datacenter performance analysis of a tensor processing unit"

Connected through hyperedge HE-7 (induced by source paper "isa mapper a compute and hardware agnostic deep learning compiler"):
  with members:
    - "communication optimal convolutional neural nets"
    - "tvm end to end optimization stack for deep learning"
    - "learning to optimize tensor programs"
    - "intel ngraph an intermediate representation compiler and executor for deep learning"
    - "tensor comprehensions framework agnostic high performance machine learning abstractions"
    - "rigid motion scattering for texture classification"

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)