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: "unifying data model and hybrid parallelism in deep learning via tensor tiling"
  Abstract: Deep learning systems have become vital tools across many fields, but the increasing model sizes mean that training must be accelerated to maintain such systems' utility. Current systems like Tensorflow and MXNet focus on one specific parallelization strategy, data parallelism, which requires large training batch sizes in order to scale. We cast the problem of finding the best parallelization strategy as the problem of finding the best tiling to partition tensors with the least overall communication. We propose an algorithm that can find the optimal tiling. Our resulting parallelization solution is a hybrid of data parallelism and model parallelism. We build the SoyBean system that performs automatic parallelization. SoyBean automatically transforms a serial dataflow graph captured by an existing deep learning system frontend into a parallel dataflow graph based on the optimal tiling it has found. Our evaluations show that SoyBean is 1.5x-4x faster than pure data parallelism for AlexNet and VGG. We present this automatic tiling in a new system, SoyBean, that can act as a backend for Tensorflow, MXNet, and others.

Connected through hyperedge HE-1 (induced by source paper "hypar towards hybrid parallelism for deep learning accelerator array"):
  with members:
    - "supporting very large models using automatic dataflow graph partitioning"
    - "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"
    - "group scissor scaling neuromorphic computing design to large neural networks"

Connected through hyperedge HE-2 (induced by source paper "hybrid data model parallel training for sequence to sequence recurrent neural network machine translation"):
  with members:
    - "marian fast neural machine translation in c"
    - "scaling neural machine translation"
    - "the university of edinburgh s neural mt systems for wmt17"
    - "attention is all you need"
    - "opennmt open source toolkit for neural machine translation"
    - "massive exploration of neural machine translation architectures"
    - "deep recurrent models with fast forward connections for neural machine translation"
    - "google s neural machine translation system bridging the gap between human and machine translation"

Connected through hyperedge HE-3 (induced by source paper "parallax sparsity aware data parallel training of deep neural networks"):
  with members:
    - "supporting very large models using automatic dataflow graph partitioning"
    - "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"
    - "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)