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: "benchmarking tpu gpu and cpu platforms for deep learning"
  Abstract: Training deep learning models is compute-intensive and there is an industry-wide trend towards hardware specialization to improve performance. To systematically benchmark deep learning platforms, we introduce ParaDnn, a parameterized benchmark suite for deep learning that generates end-to-end models for fully connected (FC), convolutional (CNN), and recurrent (RNN) neural networks. Along with six real-world models, we benchmark Google's Cloud TPU v2/v3, NVIDIA's V100 GPU, and an Intel Skylake CPU platform. We take a deep dive into TPU architecture, reveal its bottlenecks, and highlight valuable lessons learned for future specialized system design. We also provide a thorough comparison of the platforms and find that each has unique strengths for some types of models. Finally, we quantify the rapid performance improvements that specialized software stacks provide for the TPU and GPU platforms.

Connected through hyperedge HE-1 (induced by source paper "the deep learning compiler a comprehensive survey"):
  with members:
    - "shredder learning noise distributions to protect inference privacy"
    - "fusionstitching boosting execution efficiency of memory intensive computations for dl workloads"
    - "stripe tensor compilation via the nested polyhedral model"
    - "tensorflow eager a multi stage python embedded dsl for machine learning"
    - "privacy for rescue a new testimony why privacy is vulnerable in deep models"
    - "dissecting the graphcore ipu architecture via microbenchmarking"
    - "a comprehensive survey on graph neural networks"
    - "relay a high level compiler for deep learning"

Connected through hyperedge HE-2 (induced by source paper "deeprecsys a system for optimizing end to end at scale neural recommendation inference"):
  with members:
    - "the architectural implications of facebook s dnn based personalized recommendation"
    - "deep learning recommendation model for personalization and recommendation systems"
    - "mixed dimension embeddings with application to memory efficient recommendation systems"
    - "mlperf inference benchmark"
    - "prema a predictive multi task scheduling algorithm for preemptible neural processing units"
    - "tensordimm a practical near memory processing architecture for embeddings and tensor operations in deep learning"
    - "compositional embeddings using complementary partitions for memory efficient recommendation systems"
    - "mlperf training benchmark"

Connected through hyperedge HE-3 (induced by source paper "stannis low power acceleration of deep neural network training using computational storage"):
  with members:
    - "age of information in multiple sensing"
    - "horovod fast and easy distributed deep learning in tensorflow"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "tensorflow a system for large scale machine learning"
    - "deep residual learning for image recognition"
    - "a reliable effective terascale linear learning system"

Connected through hyperedge HE-4 (induced by source paper "previous a methodology for prediction of visual inference performance on iot devices"):
  with members:
    - "single path nas designing hardware efficient convnets in less than 4 hours"
    - "design automation for efficient deep learning computing"
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "effnet an efficient structure for convolutional neural networks"
    - "amc automl for model compression and acceleration on mobile devices"
    - "benchmark analysis of representative deep neural network architectures"
    - "edge intelligence on demand deep learning model co inference with device edge synergy"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"

Connected through hyperedge HE-5 (induced by source paper "meth a family of high resolution and variable shape image challenges"):
  with members:
    - "leveraging the bfloat16 artificial intelligence datatype for higher precision computations"
    - "a study of bfloat16 for deep learning training"
    - "continual lifelong learning with neural networks a review"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "exascale deep learning for climate analytics"
    - "horovod fast and easy distributed deep learning in tensorflow"
    - "high resolution breast cancer screening with multi view deep convolutional neural networks"
    - "online deep learning learning deep neural networks on the fly"

Connected through hyperedge HE-6 (induced by source paper "performance and power evaluation of ai accelerators for training deep learning models"):
  with members:
    - "automl a survey of the state of the art"
    - "the impact of gpu dvfs on the energy and performance of deep learning an empirical study"
    - "image classification at supercomputer scale"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "intel ngraph an intermediate representation compiler and executor for deep learning"
    - "in datacenter performance analysis of a tensor processing unit"
    - "gpgpu performance estimation with core and memory frequency scaling"
    - "attention is all you need"

Connected through hyperedge HE-7 (induced by source paper "benchmarking the performance and power of ai accelerators for ai training"):
  with members:
    - "automl a survey of the state of the art"
    - "the impact of gpu dvfs on the energy and performance of deep learning an empirical study"
    - "image classification at supercomputer scale"
    - "intel ngraph an intermediate representation compiler and executor for deep learning"
    - "highly scalable deep learning training system with mixed precision training imagenet in four minutes"
    - "gpgpu performance estimation with core and memory frequency scaling"
    - "attention is all you need"
    - "mixed precision training"

Connected through hyperedge HE-8 (induced by source paper "across stack profiling and characterization of machine learning models on gpus"):
  with members:
    - "a modular benchmarking infrastructure for high performance and reproducible deep learning"
    - "performance modelling of deep learning on intel many integrated core architectures"
    - "lstm benchmarks for deep learning frameworks"
    - "frustrated with replicating claims of a shared model a solution"
    - "characterizing co located datacenter workloads an alibaba case study"
    - "deep residual learning for image recognition"
    - "caffe convolutional architecture for fast feature embedding"

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)