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: "image classification at supercomputer scale"
  Abstract: Deep learning is extremely computationally intensive, and hardware vendors have responded by building faster accelerators in large clusters. Training deep learning models at petaFLOPS scale requires overcoming both algorithmic and systems software challenges. In this paper, we discuss three systems-related optimizations: (1) distributed batch normalization to control per-replica batch sizes, (2) input pipeline optimizations to sustain model throughput, and (3) 2-D torus all-reduce to speed up gradient summation. We combine these optimizations to train ResNet-50 on ImageNet to 76.3% accuracy in 2.2 minutes on a 1024-chip TPU v3 Pod with a training throughput of over 1.05 million images/second and no accuracy drop.

Connected through hyperedge HE-1 (induced by source paper "communication optimization strategies for distributed deep learning a survey"):
  with members:
    - "error feedback fixes signsgd and other gradient compression schemes"
    - "powersgd practical low rank gradient compression for distributed optimization"
    - "3lc lightweight and effective traffic compression for distributed machine learning"
    - "priority based parameter propagation for distributed dnn training"
    - "large batch training for lstm and beyond"
    - "scalable deep learning on distributed infrastructures challenges techniques and tools"
    - "yet another accelerated sgd resnet 50 training on imagenet in 74 7 seconds"
    - "redsync reducing synchronization bandwidth for distributed deep learning training system"

Connected through hyperedge HE-2 (induced by source paper "scalable and practical natural gradient for large scale deep learning"):
  with members:
    - "which algorithmic choices matter at which batch sizes insights from a noisy quadratic model"
    - "chainer a deep learning framework for accelerating the research cycle"
    - "limitations of the empirical fisher approximation"
    - "yet another accelerated sgd resnet 50 training on imagenet in 74 7 seconds"
    - "information matrices and generalization"
    - "massively distributed sgd imagenet resnet 50 training in a flash"
    - "don t use large mini batches use local sgd"
    - "highly scalable deep learning training system with mixed precision training imagenet in four minutes"

Connected through hyperedge HE-3 (induced by source paper "large batch training does not need warmup"):
  with members:
    - "large batch training for lstm and beyond"
    - "massively distributed sgd imagenet resnet 50 training in a flash"
    - "on lipschitz bounds of general convolutional neural networks"
    - "a closer look at deep learning heuristics learning rate restarts warmup and distillation"
    - "exploring the limits of weakly supervised pretraining"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "highly scalable deep learning training system with mixed precision training imagenet in four minutes"
    - "measuring the effects of data parallelism on neural network training"

Connected through hyperedge HE-4 (induced by source paper "progressive compressed records taking a byte out of deep learning data"):
  with members:
    - "dataset distillation"
    - "discrepancy coresets and sketches in machine learning"
    - "3lc lightweight and effective traffic compression for distributed machine learning"
    - "yet another accelerated sgd resnet 50 training on imagenet in 74 7 seconds"
    - "beyond the memory wall a case for memory centric hpc system for deep learning"
    - "the ham10000 dataset a large collection of multi source dermatoscopic images of common pigmented skin lesions"
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"

Connected through hyperedge HE-5 (induced by source paper "mlperf training benchmark"):
  with members:
    - "deep learning recommendation model for personalization and recommendation systems"
    - "scalable realistic recommendation datasets through fractal expansions"
    - "a modular benchmarking infrastructure for high performance and reproducible deep learning"
    - "optimizing network performance for distributed dnn training on gpu clusters imagenet alexnet training in 1 5 minutes"
    - "scalable methods for 8 bit training of neural networks"
    - "massively distributed sgd imagenet resnet 50 training in a flash"
    - "analysis of dawnbench a time to accuracy machine learning performance benchmark"
    - "nvidia tensor core programmability performance precision"

Connected through hyperedge HE-6 (induced by source paper "parameterized structured pruning for deep neural networks"):
  with members:
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"
    - "discrimination aware channel pruning for deep neural networks"
    - "layer compensated pruning for resource constrained convolutional neural networks"
    - "a convergence theory for deep learning via over parameterization"
    - "soft filter pruning for accelerating deep convolutional neural networks"
    - "in datacenter performance analysis of a tensor processing unit"
    - "learning efficient convolutional networks through network slimming"

Connected through hyperedge HE-7 (induced by source paper "spring a sparsity aware reduced precision monolithic 3d cnn accelerator architecture for training and inference"):
  with members:
    - "software defined design space exploration for an efficient ai accelerator architecture"
    - "mixed precision training of convolutional neural networks using integer operations"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "ucnn exploiting computational reuse in deep neural networks via weight repetition"
    - "highly scalable deep learning training system with mixed precision training imagenet in four minutes"
    - "deep learning inference in facebook data centers characterization performance optimizations and hardware implications"
    - "progressive neural architecture search"
    - "in datacenter performance analysis of a tensor processing unit"

Connected through hyperedge HE-8 (induced by source paper "large batch training for lstm and beyond"):
  with members:
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "mesh tensorflow deep learning for supercomputers"
    - "highly scalable deep learning training system with mixed precision training imagenet in four minutes"
    - "measuring the effects of data parallelism on neural network training"
    - "large scale distributed second order optimization using kronecker factored approximate curvature for deep convolutional neural networks"
    - "imagenet training in minutes"
    - "in datacenter performance analysis of a tensor processing unit"
    - "scaling sgd batch size to 32k for imagenet training"

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.LG(Machine Learning)