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: "low precision arithmetic for deep learning"
  Abstract: We simulate the training of a set of state of the art neural networks, the Maxout networks (Goodfellow et al., 2013a), on three benchmark datasets: the MNIST, CIFAR10 and SVHN, with three distinct arithmetics: floating point, fixed point and dynamic fixed point. For each of those datasets and for each of those arithmetics, we assess the impact of the precision of the computations on the final error of the training. We find that very low precision computation is sufficient not just for running trained networks but also for training them. For example, almost state-of-the-art results were obtained on most datasets with 10 bits for computing activations and gradients, and 12 bits for storing updated parameters.

Connected through hyperedge HE-1 (induced by source paper "elastic consistency a general consistency model for distributed stochastic gradient descent"):
  with members:
    - "error feedback fixes signsgd and other gradient compression schemes"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "communication compression for decentralized training"
    - "fault tolerance in iterative convergent machine learning"
    - "the convergence of stochastic gradient descent in asynchronous shared memory"
    - "sparsified sgd with memory"
    - "toward understanding the impact of staleness in distributed machine learning"
    - "gradient sparsification for communication efficient distributed optimization"

Connected through hyperedge HE-2 (induced by source paper "precise neural network computation with imprecise analog devices"):
  with members:
    - "binareye an always on energy accuracy scalable binary cnn processor with all memory on chip in 28nm cmos"
    - "a 128 channel extreme learning machine based neural decoder for brain machine interfaces"
    - "memory and information processing in neuromorphic systems"
    - "binaryconnect training deep neural networks with binary weights during propagations"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "training and operation of an integrated neuromorphic network based on metal oxide memristors"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "cheetah mixed low precision hardware software co design framework for dnns on the edge"):
  with members:
    - "a study of bfloat16 for deep learning training"
    - "mixed precision training with 8 bit floating point"
    - "performance efficiency trade off of low precision numerical formats in deep neural networks"
    - "drop an octave reducing spatial redundancy in convolutional neural networks with octave convolution"
    - "quantizing deep convolutional networks for efficient inference a whitepaper"
    - "deep positron a deep neural network using the posit number system"
    - "edge intelligence on demand deep learning model co inference with device edge synergy"
    - "revisiting the importance of individual units in cnns via ablation"

Connected through hyperedge HE-4 (induced by source paper "spiking neural networks for inference and learning a memristor based design perspective"):
  with members:
    - "on resistive memories one step row readout technique and sensing circuitry"
    - "biologically inspired alternatives to backpropagation through time for learning in recurrent neural nets"
    - "surrogate gradient learning in spiking neural networks"
    - "contrastive hebbian learning with random feedback weights"
    - "mitigating asymmetric nonlinear weight update effects in hardware neural network based on analog resistive synapse"
    - "synaptic plasticity dynamics for deep continuous local learning"
    - "slayer spike layer error reassignment in time"
    - "deep supervised learning using local errors"

Connected through hyperedge HE-5 (induced by source paper "autoqb automl for network quantization and binarization on mobile devices"):
  with members:
    - "amc automl for model compression and acceleration on mobile devices"
    - "alternating multi bit quantization for recurrent neural networks"
    - "discovering low precision networks close to full precision networks for efficient embedded inference"
    - "neural architecture search with bayesian optimisation and optimal transport"
    - "network sketching exploiting binary structure in deep cnns"
    - "learning transferable architectures for scalable image recognition"
    - "quantization and training of neural networks for efficient integer arithmetic only inference"
    - "towards accurate binary convolutional neural network"

Connected through hyperedge HE-6 (induced by source paper "accelerating generalized linear models with mlweaving a one size fits all system for any precision learning technical report"):
  with members:
    - "high accuracy low precision training"
    - "adaptive communication strategies to achieve the best error runtime trade off in local update sgd"
    - "in rdbms hardware acceleration of advanced analytics"
    - "bismo a scalable bit serial matrix multiplication overlay for reconfigurable computing"
    - "vibnn hardware acceleration of bayesian neural networks"
    - "e rnn design optimization for efficient recurrent neural networks in fpgas"
    - "neural cache bit serial in cache acceleration of deep neural networks"
    - "in datacenter performance analysis of a tensor processing unit"

Connected through hyperedge HE-7 (induced by source paper "deep learning training on the edge with low precision posits"):
  with members:
    - "a study of bfloat16 for deep learning training"
    - "template based posit multiplication for training and inferring in neural networks"
    - "mixed precision training with 8 bit floating point"
    - "performance efficiency trade off of low precision numerical formats in deep neural networks"
    - "drop an octave reducing spatial redundancy in convolutional neural networks with octave convolution"
    - "deep positron a deep neural network using the posit number system"
    - "edge intelligence on demand deep learning model co inference with device edge synergy"
    - "revisiting the importance of individual units in cnns via ablation"

Connected through hyperedge HE-8 (induced by source paper "efficient memory management for gpu based deep learning systems"):
  with members:
    - "training wide residual networks for deployment using a single bit for each weight"
    - "superneurons dynamic gpu memory management for training deep neural networks"
    - "ease ml towards multi tenant resource sharing for machine learning workloads"
    - "memory efficient implementation of densenets"
    - "comparative study of deep learning software frameworks"
    - "lifetime based memory management for distributed data processing systems"
    - "database meets deep learning challenges and opportunities"
    - "eie efficient inference engine on compressed deep neural network"

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)