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 storage for deep learning"
  Abstract: We train 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 storing formats: floating point, fixed point and dynamic fixed point. For each of those datasets and for each of those formats, we assess the impact of the precision of the storage on the final error of the training. We find that very low precision storage is sufficient not just for running trained networks but also for training them. For example, Maxout networks state-of-the-art results are nearly maintained with 10 bits for storing activations and gradients, and 12 bits for storing parameters.

Connected through hyperedge HE-1 (induced by source paper "towards explainable bit error tolerance of resistive ram based binarized neural networks"):
  with members:
    - "outstanding bit error tolerance of resistive ram based binarized neural networks"
    - "espnetv2 a light weight power efficient and general purpose convolutional neural network"
    - "bmxnet an open source binary neural network implementation based on mxnet"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "xnor net imagenet classification using binary convolutional neural networks"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "fixed point quantization of deep convolutional networks"
    - "deep learning with limited numerical precision"

Connected through hyperedge HE-2 (induced by source paper "nvidia tensor core programmability performance precision"):
  with members:
    - "in datacenter performance analysis of a tensor processing unit"
    - "mixed precision training"
    - "flexpoint an adaptive numerical format for efficient training of deep neural networks"
    - "on the strong scaling of the spectral element solver nek5000 on petascale systems"
    - "deep learning with limited numerical precision"
    - "training deep neural networks with low precision multiplications"
    - "cudnn efficient primitives for deep learning"

Connected through hyperedge HE-3 (induced by source paper "learning compact recurrent neural networks"):
  with members:
    - "lstm a search space odyssey"
    - "compressing neural networks with the hashing trick"
    - "deep learning with limited numerical precision"

Connected through hyperedge HE-4 (induced by source paper "compressing neural networks with the hashing trick"):
  with members:
    - "compressed support vector machines"
    - "deep learning with limited numerical precision"
    - "exploiting linear structure within convolutional networks for efficient evaluation"
    - "training deep neural networks with low precision multiplications"
    - "deep visual semantic alignments for generating image descriptions"
    - "low precision arithmetic for deep learning"
    - "cnn features off the shelf an astounding baseline for recognition"
    - "show and tell a neural image caption generator"

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)