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: "integrated model batch and domain parallelism in training neural networks"
  Abstract: We propose a new integrated method of exploiting model, batch and domain parallelism for the training of deep neural networks (DNNs) on large distributed-memory computers using minibatch stochastic gradient descent (SGD). Our goal is to find an efficient parallelization strategy for a fixed batch size using $P$ processes. Our method is inspired by the communication-avoiding algorithms in numerical linear algebra. We see $P$ processes as logically divided into a $P_r \times P_c$ grid where the $P_r$ dimension is implicitly responsible for model/domain parallelism and the $P_c$ dimension is implicitly responsible for batch parallelism. In practice, the integrated matrix-based parallel algorithm encapsulates these types of parallelism automatically. We analyze the communication complexity and analytically demonstrate that the lowest communication costs are often achieved neither with pure model nor with pure data parallelism. We also show how the domain parallel approach can help in extending the theoretical scaling limit of the typical batch parallel method.

Connected through hyperedge HE-1 (induced by source paper "flexible communication avoiding matrix multiplication on fpga with high level synthesis"):
  with members:
    - "red blue pebbling revisited near optimal parallel matrix matrix multiplication"
    - "hlslib software engineering for hardware design"
    - "demystifying parallel and distributed deep learning an in depth concurrency analysis"
    - "communication optimal convolutional neural nets"
    - "communication lower bounds for distributed memory computations"
    - "communication lower bounds for distributed memory computations"
    - "hitting the memory wall implications of the obvious"

Connected through hyperedge HE-2 (induced by source paper "checkmate breaking the memory wall with optimal tensor rematerialization"):
  with members:
    - "the ooo vliw jit compiler for gpu inference"
    - "transformer xl attentive language models beyond a fixed length context"
    - "generating long sequences with sparse transformers"
    - "roberta a robustly optimized bert pretraining approach"
    - "pytorch an imperative style high performance deep learning library"
    - "group normalization"
    - "beyond data and model parallelism for deep neural networks"
    - "an empirical model of large batch training"

Connected through hyperedge HE-3 (induced by source paper "leader stochastic gradient descent for distributed training of deep learning models"):
  with members:
    - "demystifying parallel and distributed deep learning an in depth concurrency analysis"
    - "understanding the loss surface of neural networks for binary classification"
    - "horovod fast and easy distributed deep learning in tensorflow"
    - "complete dictionary recovery over the sphere i overview and the geometric picture"
    - "parle parallelizing stochastic gradient descent"
    - "scaling sgd batch size to 32k for imagenet training"
    - "the power of interpolation understanding the effectiveness of sgd in modern over parametrized learning"
    - "no spurious local minima in nonconvex low rank problems a unified geometric analysis"

Connected through hyperedge HE-4 (induced by source paper "improving strong scaling of cnn training by exploiting finer grained parallelism"):
  with members:
    - "demystifying parallel and distributed deep learning an in depth concurrency analysis"
    - "communication optimal convolutional neural nets"
    - "exploring the limits of weakly supervised pretraining"
    - "superneurons dynamic gpu memory management for training deep neural networks"
    - "measuring the effects of data parallelism on neural network training"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "imagenet training in minutes"
    - "an analysis of deep neural network models for practical applications"

Connected through hyperedge HE-5 (induced by source paper "inefficiency of k fac for large batch size training"):
  with members:
    - "parameter re initialization through cyclical batch size schedules"
    - "implicit self regularization in deep neural networks evidence from random matrix theory and implications for learning"
    - "an empirical model of large batch training"
    - "three mechanisms of weight decay regularization"
    - "hessian based analysis of large batch training and robustness to adversaries"
    - "large batch size training of neural networks with adversarial training and second order information"
    - "highly scalable deep learning training system with mixed precision training imagenet in four minutes"
    - "on the computational inefficiency of large batch sizes for stochastic gradient descent"

Connected through hyperedge HE-6 (induced by source paper "hypar flow exploiting mpi and keras for scalable hybrid parallel dnn training using tensorflow"):
  with members:
    - "improving strong scaling of cnn training by exploiting finer grained parallelism"
    - "tensorflow eager a multi stage python embedded dsl for machine learning"
    - "demystifying parallel and distributed deep learning an in depth concurrency analysis"
    - "beyond data and model parallelism for deep neural networks"
    - "regularized evolution for image classifier architecture search"
    - "gpipe efficient training of giant neural networks using pipeline parallelism"
    - "mesh tensorflow deep learning for supercomputers"
    - "highly scalable deep learning training system with mixed precision training imagenet in four minutes"

Connected through hyperedge HE-7 (induced by source paper "distributed equivalent substitution training for large scale recommender systems"):
  with members:
    - "large batch optimization for deep learning training bert in 76 minutes"
    - "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"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "large batch size training of neural networks with adversarial training and second order information"):
  with members:
    - "traditional and heavy tailed self regularization in neural network models"
    - "oversketched newton fast convex optimization for serverless systems"
    - "pyhessian neural networks through the lens of the hessian"
    - "inefficiency of k fac for large batch size training"
    - "massively distributed sgd imagenet resnet 50 training in a flash"
    - "implicit self regularization in deep neural networks evidence from random matrix theory and implications for learning"
    - "an empirical model of large batch training"
    - "image classification at supercomputer scale"

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)