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: "characterizing deep learning i o workloads in tensorflow"
  Abstract: The performance of Deep-Learning (DL) computing frameworks rely on the performance of data ingestion and checkpointing. In fact, during the training, a considerable high number of relatively small files are first loaded and pre-processed on CPUs and then moved to accelerator for computation. In addition, checkpointing and restart operations are carried out to allow DL computing frameworks to restart quickly from a checkpoint. Because of this, I/O affects the performance of DL applications. In this work, we characterize the I/O performance and scaling of TensorFlow, an open-source programming framework developed by Google and specifically designed for solving DL problems. To measure TensorFlow I/O performance, we first design a micro-benchmark to measure TensorFlow reads, and then use a TensorFlow mini-application based on AlexNet to measure the performance cost of I/O and checkpointing in TensorFlow. To improve the checkpointing performance, we design and implement a burst buffer. We find that increasing the number of threads increases TensorFlow bandwidth by a maximum of 2.3× and 7.8× on our benchmark environments. The use of the tensorFlow prefetcher results in a complete overlap of computation on accelerator and input pipeline on CPU eliminating the effective cost of I/O on the overall performance. The use of a burst buffer to checkpoint to a fast small capacity storage and copy asynchronously the checkpoints to a slower large capacity storage resulted in a performance improvement of 2.6× with respect to checkpointing directly to slower storage on our benchmark environment.

Connected through hyperedge HE-1 (induced by source paper "high performance i o for large scale deep learning"):
  with members:
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "comparative study of deep learning software frameworks"

Connected through hyperedge HE-2 (induced by source paper "characterizing deep learning training workloads on alibaba pai"):
  with members:
    - "scalable deep learning on distributed infrastructures challenges techniques and tools"
    - "data motif based proxy benchmarks for big data and ai workloads"
    - "graph convolutional neural networks for web scale recommender systems"
    - "communication scheduling as a first class citizen in distributed machine learning systems"
    - "data motifs a lens towards fully understanding big data and ai workloads"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "fusionstitching deep fusion and code generation for tensorflow computations on gpus"
    - "predicting the computational cost of deep learning models"

Connected through hyperedge HE-3 (induced by source paper "efficient adversarial training with transferable adversarial examples"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "adversarial training for free"
    - "using pre training can improve model robustness and uncertainty"
    - "universal adversarial training"
    - "adversarial robustness toolbox v1 0 0"
    - "certified defenses against adversarial examples"
    - "learning transferable adversarial examples via ghost networks"
    - "robust physical world attacks on deep learning models"

Connected through hyperedge HE-4 (induced by source paper "faster neural network training with data echoing"):
  with members:
    - "image classification at supercomputer scale"
    - "not all samples are created equal deep learning with importance sampling"
    - "augment your batch better training with larger batches"
    - "measuring the effects of data parallelism on neural network training"
    - "critical hyper parameters no random no cry"
    - "attention is all you need"
    - "faster sgd training by minibatch persistency"
    - "ssd single shot multibox detector"

Connected through hyperedge HE-5 (induced by source paper "tensorflow doing hpc"):
  with members:
    - "rpc considered harmful fast distributed deep learning on rdma"
    - "nvidia tensor core programmability performance precision"
    - "designing a micro benchmark suite to evaluate grpc for tensorflow early experiences"
    - "horovod fast and easy distributed deep learning in tensorflow"
    - "in datacenter performance analysis of a tensor processing unit"
    - "distributed tensorflow with mpi"
    - "tensorflow a system for large scale machine learning"
    - "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.DC(Distributed, Parallel, and Cluster Computing)