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: "8 bit approximations for parallelism in deep learning"
  Abstract: The creation of practical deep learning data-products often requires parallelization across processors and computers to make deep learning feasible on large data sets, but bottlenecks in communication bandwidth make it difficult to attain good speedups through parallelism. Here we develop and test 8-bit approximation algorithms which make better use of the available bandwidth by compressing 32-bit gradients and nonlinear activations to 8-bit approximations. We show that these approximations do not decrease predictive performance on MNIST, CIFAR10, and ImageNet for both model and data parallelism and provide a data transfer speedup of 2x relative to 32-bit parallelism. We build a predictive model for speedups based on our experimental data, verify its validity on known speedup data, and show that we can obtain a speedup of 50x and more on a system of 96 GPUs compared to a speedup of 23x for 32-bit. We compare our data types with other methods and show that 8-bit approximations achieve state-of-the-art speedups for model parallelism. Thus 8-bit approximation is an efficient method to parallelize convolutional networks on very large systems of GPUs.

Connected through hyperedge HE-1 (induced by source paper "learning home crowdsourced training of large neural networks using decentralized mixture of experts"):
  with members:
    - "scaling laws for neural language models"
    - "zero memory optimization towards training a trillion parameter models"
    - "megatron lm training multi billion parameter language models using model parallelism"
    - "natural compression for distributed deep learning"
    - "defending against neural fake news"
    - "large scale learning of general visual representations for transfer"
    - "optimizing network performance for distributed dnn training on gpu clusters imagenet alexnet training in 1 5 minutes"
    - "pipemare asynchronous pipeline parallel dnn training"

Connected through hyperedge HE-2 (induced by source paper "distributed learning of deep neural networks using independent subnet training"):
  with members:
    - "powersgd practical low rank gradient compression for distributed optimization"
    - "mlsys the new frontier of machine learning systems"
    - "large batch training for lstm and beyond"
    - "communication efficient distributed sgd with sketching"
    - "reducing bert pre training time from 3 days to 76 minutes"
    - "inefficiency of k fac for large batch size training"
    - "survey of dropout methods for deep neural networks"
    - "on the ineffectiveness of variance reduced optimization for deep learning"

Connected through hyperedge HE-3 (induced by source paper "full stack optimization for accelerating cnns with fpga validation"):
  with members:
    - "packing sparse convolutional neural networks for efficient systolic array implementations column combining under joint optimization"
    - "quantizing deep convolutional networks for efficient inference a whitepaper"
    - "from hashing to cnns training binaryweight networks via hashing"
    - "design flow of accelerating hybrid extremely low bit width neural network in embedded fpga"
    - "bi real net enhancing the performance of 1 bit cnns with improved representational capability and advanced training algorithm"
    - "pact parameterized clipping activation for quantized neural networks"
    - "block sparse recurrent neural networks"
    - "in datacenter performance analysis of a tensor processing unit"

Connected through hyperedge HE-4 (induced by source paper "a study of bfloat16 for deep learning training"):
  with members:
    - "deep learning recommendation model for personalization and recommendation systems"
    - "high performance monte carlo simulation of ising model on tpu clusters"
    - "mixed precision training of convolutional neural networks using integer operations"
    - "anatomy of high performance deep learning convolutions on simd architectures"
    - "automatic full compilation of julia programs and ml models to cloud tpus"
    - "deep cross network for ad click predictions"
    - "mixed precision training"
    - "ternary neural networks with fine grained quantization"

Connected through hyperedge HE-5 (induced by source paper "on the discrepancy between the theoretical analysis and practical implementations of compressed communication for distributed deep learning"):
  with members:
    - "fast and faster convergence of sgd for over parameterized models and an accelerated perceptron"
    - "communication compression for decentralized training"
    - "methods for interpreting and understanding deep neural networks"
    - "signsgd compressed optimisation for non convex problems"
    - "adacomp adaptive residual gradient compression for data parallel distributed training"
    - "performance modeling and evaluation of distributed deep learning frameworks on gpus"
    - "terngrad ternary gradients to reduce communication in distributed deep learning"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-6 (induced by source paper "λ nic interactive serverless compute on programmable smartnics"):
  with members:
    - "scalable methods for 8 bit training of neural networks"
    - "homa a receiver driven low latency transport protocol using network priorities"
    - "comparative study of deep learning software frameworks"

Connected through hyperedge HE-7 (induced by source paper "recent advances in efficient computation of deep convolutional neural networks"):
  with members:
    - "from hashing to cnns training binaryweight networks via hashing"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "in datacenter performance analysis of a tensor processing unit"
    - "learning efficient convolutional networks through network slimming"
    - "scnn an accelerator for compressed sparse convolutional neural networks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "thinet a filter level pruning method for deep neural network compression"
    - "deep learning with low precision by half wave gaussian quantization"

Connected through hyperedge HE-8 (induced by source paper "effective quantization approaches for recurrent neural networks"):
  with members:
    - "low precision rnns quantizing rnns without losing accuracy"
    - "balanced quantization an effective and efficient approach to quantized neural networks"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "a survey on deep learning in medical image analysis"
    - "binarized neural networks"
    - "effective quantization methods for recurrent neural networks"
    - "ternary weight networks"
    - "bitwise neural 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.NE(Neural and Evolutionary Computing)