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: "supporting very large models using automatic dataflow graph partitioning"
  Abstract: This paper presents Tofu, a system that partitions very large DNN models across multiple GPU devices to reduce per-GPU memory footprint. Tofu is designed to partition a dataflow graph of fine-grained tensor operators used by platforms like MXNet and TensorFlow. In order to automatically partition each operator, we propose to describe the semantics of an operator in a simple language inspired by Halide. To optimally partition different operators in a dataflow graph, Tofu uses a recursive search algorithm that minimizes the total communication cost. Our experiments on an 8-GPU machine show that Tofu enables the training of very large CNN and RNN models. It also achieves 25% - 400% speedup over alternative approaches to train very large models.

Connected through hyperedge HE-1 (induced by source paper "heterogeneity aware asynchronous decentralized training"):
  with members:
    - "hop heterogeneity aware decentralized training"
    - "yet another accelerated sgd resnet 50 training on imagenet in 74 7 seconds"
    - "optimizing network performance for distributed dnn training on gpu clusters imagenet alexnet training in 1 5 minutes"
    - "pipe sgd a decentralized pipelined sgd framework for distributed deep net training"
    - "demystifying parallel and distributed deep learning an in depth concurrency analysis"
    - "communication compression for decentralized training"
    - "beyond data and model parallelism for deep neural networks"
    - "adaptive communication strategies to achieve the best error runtime trade off in local update sgd"

Connected through hyperedge HE-2 (induced by source paper "hypar towards hybrid parallelism for deep learning accelerator array"):
  with members:
    - "red a reram based deconvolution accelerator"
    - "mt spike a multilayer time based spiking neuromorphic architecture with temporal error backpropagation"
    - "pt spike a precise time dependent single spike neuromorphic architecture with efficient supervised learning"
    - "ucnn exploiting computational reuse in deep neural networks via weight repetition"
    - "vibnn hardware acceleration of bayesian neural networks"
    - "revisiting small batch training for deep neural networks"
    - "unifying data model and hybrid parallelism in deep learning via tensor tiling"
    - "group scissor scaling neuromorphic computing design to large neural networks"

Connected through hyperedge HE-3 (induced by source paper "buddy compression enabling larger memory for deep learning and hpc workloads on gpus"):
  with members:
    - "delta gpu performance model for deep learning applications with in depth memory system traffic analysis"
    - "high accuracy low precision training"
    - "pfdet 2nd place solution to open images challenge 2018 object detection track"
    - "dynamic sparse graph for efficient deep learning"
    - "measuring the effects of data parallelism on neural network training"
    - "dissecting the nvidia volta gpu architecture via microbenchmarking"
    - "megdet a large mini batch object detector"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"

Connected through hyperedge HE-4 (induced by source paper "training on the edge the why and the how"):
  with members:
    - "gpipe efficient training of giant neural networks using pipeline parallelism"
    - "high level python abstractions for optimal checkpointing in inversion problems"
    - "cutting down training memory by re fowarding"
    - "optimal gradient checkpoint search for arbitrary computation graphs"
    - "training deep nets with sublinear memory cost"
    - "on large batch training for deep learning generalization gap and sharp minima"
    - "mxnet a flexible and efficient machine learning library for heterogeneous distributed systems"

Connected through hyperedge HE-5 (induced by source paper "parallax sparsity aware data parallel training of deep neural networks"):
  with members:
    - "autopruner an end to end trainable filter pruning method for efficient deep model inference"
    - "mxnet mpi embedding mpi parallelism in parameter server task model for scaling deep learning"
    - "highly scalable deep learning training system with mixed precision training imagenet in four minutes"
    - "unifying data model and hybrid parallelism in deep learning via tensor tiling"
    - "horovod fast and easy distributed deep learning in tensorflow"
    - "pipedream fast and efficient pipeline parallel dnn training"
    - "poseidon an efficient communication architecture for distributed deep learning on gpu clusters"
    - "efficient training of convolutional neural nets on large distributed systems"

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)