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 batch optimization for deep learning training bert in 76 minutes"
  Abstract: Training large deep neural networks on massive datasets is very challenging. One promising approach to tackle this issue is through the use of large batch stochastic optimization. However, our understanding of this approach in the context of deep learning is still very limited. Furthermore, the current approaches in this direction are heavily hand-tuned. To this end, we first study a general adaptation strategy to accelerate training of deep neural networks using large minibatches. Using this strategy, we develop a new layer-wise adaptive large batch optimization technique called LAMB. We also provide a formal convergence analysis of LAMB as well as the previous published layerwise optimizer LARS, showing convergence to a stationary point in general nonconvex settings. Our empirical results demonstrate the superior performance of LAMB for BERT and ResNet-50 training. In particular, for BERT training, our optimization technique enables use of very large batches sizes of 32868; thereby, requiring just 8599 iterations to train (as opposed to 1 million iterations in the original paper). By increasing the batch size to the memory limit of a TPUv3 pod, BERT training time can be reduced from 3 days to 76 minutes. Finally, we also demonstrate that LAMB outperforms previous large-batch training algorithms for ResNet-50 on ImageNet; obtaining state-of-the-art performance in just a few minutes.

Connected through hyperedge HE-1 (induced by source paper "train large then compress rethinking model size for efficient training and inference of transformers"):
  with members:
    - "scaling laws for neural language models"
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "are sixteen heads really better than one"
    - "analyzing multi head self attention specialized heads do the heavy lifting the rest can be pruned"
    - "distilbert a distilled version of bert smaller faster cheaper and lighter"
    - "checkmate breaking the memory wall with optimal tensor rematerialization"
    - "megatron lm training multi billion parameter language models using model parallelism"

Connected through hyperedge HE-2 (induced by source paper "benchmark performance of machine and deep learning based methodologies for urdu text document classification"):
  with members:
    - "on correlation of features extracted by deep neural networks"
    - "a simple joint model for improved contextual neural lemmatization"
    - "reducing bert pre training time from 3 days to 76 minutes"
    - "word embeddings a survey"
    - "a robust hybrid approach for textual document classification"
    - "character n gram embeddings to improve rnn language models"
    - "roberta a robustly optimized bert pretraining approach"
    - "deep contextualized word representations"

Connected through hyperedge HE-3 (induced by source paper "featurenms non maximum suppression by learning feature embeddings"):
  with members:
    - "reducing bert pre training time from 3 days to 76 minutes"
    - "occlusion aware r cnn detecting pedestrians in a crowd"
    - "yolov3 an incremental improvement"
    - "crowdhuman a benchmark for detecting human in a crowd"
    - "sampling matters in deep embedding learning"
    - "deep learning based large scale visual recommendation and search for e commerce"
    - "soft nms improving object detection with one line of code"
    - "focal loss for dense object detection"

Connected through hyperedge HE-4 (induced by source paper "cbag conditional biomedical abstract generation"):
  with members:
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "transfer learning in biomedical natural language processing an evaluation of bert and elmo on ten benchmarking datasets"
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "language models as knowledge bases"
    - "the deepfake detection challenge dfdc preview dataset"
    - "ernie enhanced representation through knowledge integration"
    - "ctrl a conditional transformer language model for controllable generation"
    - "reducing bert pre training time from 3 days to 76 minutes"

Connected through hyperedge HE-5 (induced by source paper "training large neural networks with constant memory using a new execution algorithm"):
  with members:
    - "reformer the efficient transformer"
    - "zero memory optimization towards training a trillion parameter models"
    - "huggingface s transformers state of the art natural language processing"
    - "reducing bert pre training time from 3 days to 76 minutes"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "gpipe efficient training of giant neural networks using pipeline parallelism"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "pipedream fast and efficient pipeline parallel dnn training"

Connected through hyperedge HE-6 (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-7 (induced by source paper "local adaalter communication efficient stochastic gradient descent with adaptive learning rates"):
  with members:
    - "error feedback fixes signsgd and other gradient compression schemes"
    - "reducing bert pre training time from 3 days to 76 minutes"
    - "communication efficient distributed blockwise momentum sgd with error feedback"
    - "pipe sgd a decentralized pipelined sgd framework for distributed deep net training"
    - "a sufficient condition for convergences of adam and rmsprop"
    - "federated learning strategies for improving communication efficiency"
    - "don t use large mini batches use local sgd"
    - "sparsified sgd with memory"

Connected through hyperedge HE-8 (induced by source paper "distributed equivalent substitution training for large scale recommender systems"):
  with members:
    - "xdeepfm combining explicit and implicit feature interactions for recommender systems"
    - "highly scalable deep learning training system with mixed precision training imagenet in four minutes"
    - "exploring hidden dimensions in parallelizing convolutional neural networks"
    - "integrated model batch and domain parallelism in training neural networks"
    - "device placement optimization with reinforcement learning"
    - "revisiting distributed synchronous sgd"
    - "deepfm a factorization machine based neural network for ctr prediction"
    - "accurate large minibatch sgd training imagenet in 1 hour"

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)