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: "efficient and robust parallel dnn training through model parallelism on multi gpu platform"
  Abstract: The training process of Deep Neural Network (DNN) is compute-intensive, often taking days to weeks to train a DNN model. Therefore, parallel execution of DNN training on GPUs is a widely adopted approach to speed up process nowadays. Due to the implementation simplicity, data parallelism is currently the most commonly used parallelization method. Nonetheless, data parallelism suffers from excessive inter-GPU communication overhead due to frequent weight synchronization among GPUs. Another approach is model parallelism, which partitions model among GPUs. This approach can significantly reduce inter-GPU communication cost compared to data parallelism, however, maintaining load balance is a challenge. Moreover, model parallelism faces the staleness issue; that is, gradients are computed with stale weights. In this paper, we propose a novel model parallelism method, which achieves load balance by concurrently executing forward and backward passes of two batches, and resolves the staleness issue with weight prediction. The experimental results show that our proposal achieves up to 15.77x speedup compared to data parallelism and up to 2.18x speedup compared to the state-of-the-art model parallelism method without incurring accuracy loss.

Connected through hyperedge HE-1 (induced by source paper "megatron lm training multi billion parameter language models using model parallelism"):
  with members:
    - "generalization through memorization nearest neighbor language models"
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "dynamic evaluation of transformer language models"
    - "spanbert improving pre training by representing and predicting spans"
    - "multi task deep neural networks for natural language understanding"
    - "transformer xl attentive language models beyond a fixed length context"
    - "albert a lite bert for self supervised learning of language representations"
    - "reducing bert pre training time from 3 days to 76 minutes"

Connected through hyperedge HE-2 (induced by source paper "thanks for nothing predicting zero valued activations with lightweight convolutional neural networks"):
  with members:
    - "divide and conquer leveraging intermediate feature representations for quantized training of neural networks"
    - "spatial correlation and value prediction in convolutional neural networks"
    - "mnasnet platform aware neural architecture search for mobile"
    - "dynamic channel pruning feature boosting and suppression"
    - "an analysis of deep neural network models for practical applications"
    - "sparsenn an energy efficient neural network accelerator exploiting input and output sparsity"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "scnn an accelerator for compressed sparse convolutional neural networks"

Connected through hyperedge HE-3 (induced by source paper "xpipe efficient pipeline model parallelism for multi gpu dnn training"):
  with members:
    - "deep audio visual speech recognition"
    - "optimizing multi gpu parallelization strategies for deep learning training"
    - "ampnet asynchronous model parallel training for dynamic neural networks"
    - "demystifying parallel and distributed deep learning an in depth concurrency analysis"
    - "decoupled parallel backpropagation with convergence guarantee"
    - "training neural networks using features replay"
    - "gpipe efficient training of giant neural networks using pipeline parallelism"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"

Connected through hyperedge HE-4 (induced by source paper "gpipe efficient training of giant neural networks using pipeline parallelism"):
  with members:
    - "lingvo a modular and scalable framework for sequence to sequence modeling"
    - "pay less attention with lightweight and dynamic convolutions"
    - "massively multilingual neural machine translation in the wild findings and challenges"
    - "fixup initialization residual learning without normalization"
    - "object part attention model for fine grained image classification"
    - "large scale fine grained categorization and domain specific transfer learning"
    - "do better imagenet models transfer better"
    - "analysis of dawnbench a time to accuracy machine learning performance benchmark"

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)