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: "large scale language modeling converging on 40gb of text in four hours"
  Abstract: Recent work has shown how to train Convolutional Neural Networks (CNNs) rapidly on large image datasets, then transfer the knowledge gained from these models to a variety of tasks. Following [Radford 2017], in this work, we demonstrate similar scalability and transfer for Recurrent Neural Networks (RNNs) for Natural Language tasks. By utilizing mixed precision arithmetic and a 32k batch size distributed across 128 NVIDIA Tesla V100 GPUs, we are able to train a character-level 4096-dimension multiplicative LSTM (mLSTM) for unsupervised text reconstruction over 3 epochs of the 40 GB Amazon Reviews dataset in four hours. This runtime compares favorably with previous work taking one month to train the same size and configuration for one epoch over the same dataset. Converging large batch RNN models can be challenging. Recent work has suggested scaling the learning rate as a function of batch size, but we find that simply scaling the learning rate as a function of batch size leads either to significantly worse convergence or immediate divergence for this problem. We provide a learning rate schedule that allows our model to converge with a 32k batch size. Since our model converges over the Amazon Reviews dataset in hours, and our compute requirement of 128 Tesla V100 GPUs, while substantial, is commercially available, this work opens up large scale unsupervised NLP training to most commercial applications and deep learning researchers. A model can be trained over most public or private text datasets overnight.

Connected through hyperedge HE-1 (induced by source paper "prunetrain fast neural network training by dynamic sparse model reconfiguration"):
  with members:
    - "mini batch serialization cnn training with inter layer data reuse"
    - "amc automl for model compression and acceleration on mobile devices"
    - "imagenet training in minutes"
    - "focal loss for dense object detection"
    - "don t decay the learning rate increase the batch size"
    - "learning intrinsic sparse structures within long short term memory"
    - "squeeze and excitation networks"
    - "mixed precision training"

Connected through hyperedge HE-2 (induced by source paper "compressing gradient optimizers via count sketches"):
  with members:
    - "extreme classification in log memory"
    - "scaling neural machine translation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "adafactor adaptive learning rates with sublinear memory cost"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "hash embeddings for efficient word representations"
    - "breaking the softmax bottleneck a high rank rnn language model"
    - "attention is all you need"

Connected through hyperedge HE-3 (induced by source paper "generating sentiment preserving fake online reviews using neural language models and their human and machine based detection"):
  with members:
    - "release strategies and the social impacts of language models"
    - "gltr statistical detection and visualization of generated text"
    - "defending against neural fake news"
    - "roberta a robustly optimized bert pretraining approach"
    - "neural text generation past present and beyond"
    - "deep text classification can be fooled"
    - "simple fusion return of the language model"
    - "towards diverse text generation with inverse reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "beyond human level accuracy computational challenges in deep learning"):
  with members:
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "imagenet training in minutes"
    - "deep gradient compression reducing the communication bandwidth for distributed training"
    - "exploring neural transducers for end to end speech recognition"
    - "nearly tight vc dimension and pseudodimension bounds for piecewise linear neural networks"
    - "terngrad ternary gradients to reduce communication in distributed deep learning"
    - "deep learning scaling is predictable empirically"

Connected through hyperedge HE-5 (induced by source paper "distributed deep learning strategies for automatic speech recognition"):
  with members:
    - "scaling neural machine translation"
    - "training deep neural networks with 8 bit floating point numbers"
    - "highly scalable deep learning training system with mixed precision training imagenet in four minutes"
    - "english conversational telephone speech recognition by humans and machines"
    - "adacomp adaptive residual gradient compression for data parallel distributed training"
    - "scaling sgd batch size to 32k for imagenet training"
    - "revisiting distributed synchronous sgd"
    - "terngrad ternary gradients to reduce communication in distributed deep learning"

Connected through hyperedge HE-6 (induced by source paper "an empirical model of large batch training"):
  with members:
    - "measuring the intrinsic dimension of objective landscapes"
    - "understanding short horizon bias in stochastic meta optimization"
    - "accelerated methods for deep reinforcement learning"
    - "distributed prioritized experience replay"
    - "gradient descent happens in a tiny subspace"
    - "measuring the effects of data parallelism on neural network training"
    - "imagenet training in minutes"
    - "don t decay the learning rate increase the batch size"

Connected through hyperedge HE-7 (induced by source paper "scaling neural machine translation"):
  with members:
    - "weighted transformer network for machine translation"
    - "self attention with relative position representations"
    - "high accuracy low precision training"
    - "a call for clarity in reporting bleu scores"
    - "pieces of eight 8 bit neural machine translation"
    - "the best of both worlds combining recent advances in neural machine translation"
    - "attention is all you need"
    - "don t decay the learning rate increase the batch size"

Connected through hyperedge HE-8 (induced by source paper "on the computational inefficiency of large batch sizes for stochastic gradient descent"):
  with members:
    - "hessian based analysis of large batch training and robustness to adversaries"
    - "three factors influencing minima in sgd"
    - "highly scalable deep learning training system with mixed precision training imagenet in four minutes"
    - "imagenet training in minutes"
    - "scaling sgd batch size to 32k for imagenet training"
    - "don t decay the learning rate increase the batch size"
    - "stochastic gradient descent performs variational inference converges to limit cycles for deep networks"
    - "dilated residual networks"

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)