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: "on the efficient representation and execution of deep acoustic models"
  Abstract: In this paper we present a simple and computationally efficient quantization scheme that enables us to reduce the resolution of the parameters of a neural network from 32-bit floating point values to 8-bit integer values. The proposed quantization scheme leads to significant memory savings and enables the use of optimized hardware instructions for integer arithmetic, thus significantly reducing the cost of inference. Finally, we propose a "quantization aware" training process that applies the proposed scheme during network training and find that it allows us to recover most of the loss in accuracy introduced by quantization. We validate the proposed techniques by applying them to a long short-term memory-based acoustic model on an open-ended large vocabulary speech recognition task.

Connected through hyperedge HE-1 (induced by source paper "data free quantization through weight equalization and bias correction"):
  with members:
    - "equi normalization of neural networks"
    - "same same but different recovering neural network quantization error through weight factorization"
    - "fighting quantization bias with bias"
    - "quantizing deep convolutional networks for efficient inference a whitepaper"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "a quantization friendly separable convolution for mobilenets"
    - "probabilistic binary neural networks"
    - "relaxed quantization for discretized neural networks"

Connected through hyperedge HE-2 (induced by source paper "optimizing speech recognition for the edge"):
  with members:
    - "stabilizing the lottery ticket hypothesis"
    - "lingvo a modular and scalable framework for sequence to sequence modeling"
    - "streaming end to end speech recognition for mobile devices"
    - "toward domain invariant speech recognition via large scale training"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "the unreasonable effectiveness of the forget gate"
    - "snip single shot network pruning based on connection sensitivity"
    - "rethinking the value of network pruning"

Connected through hyperedge HE-3 (induced by source paper "streaming end to end speech recognition for mobile devices"):
  with members:
    - "exploring architectures data and units for streaming end to end speech recognition with rnn transducer"
    - "in datacenter performance analysis of a tensor processing unit"
    - "parallel wavenet fast high fidelity speech synthesis"
    - "state of the art speech recognition with sequence to sequence models"
    - "streaming small footprint keyword spotting using sequence to sequence models"
    - "personalized speech recognition on mobile devices"
    - "neural speech recognizer acoustic to word lstm model for large vocabulary speech recognition"
    - "listen attend and spell"

Connected through hyperedge HE-4 (induced by source paper "west word encoded sequence transducers"):
  with members:
    - "federated learning strategies for improving communication efficiency"
    - "groupreduce block wise low rank approximation for neural language model shrinking"
    - "character word lstm language models"
    - "fasttext zip compressing text classification models"
    - "personalized speech recognition on mobile devices"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "federated optimization distributed optimization beyond the datacenter"

Connected through hyperedge HE-5 (induced by source paper "balanced quantization an effective and efficient approach to quantized neural networks"):
  with members:
    - "dorefa net training low bitwidth convolutional neural networks with low bitwidth gradients"
    - "deep neural networks are robust to weight binarization and other non linear distortions"
    - "hardware oriented approximation of convolutional neural networks"
    - "identity mappings in deep residual networks"
    - "fpga based low power speech recognition with recurrent neural networks"
    - "bitwise neural networks"
    - "fast compact and high quality lstm rnn based statistical parametric speech synthesizers for mobile devices"
    - "xnor net imagenet classification using binary convolutional neural networks"

Connected through hyperedge HE-6 (induced by source paper "streaming small footprint keyword spotting using sequence to sequence models"):
  with members:
    - "lstm a search space odyssey"
    - "optimizing expected word error rate via sampling for speech recognition"
    - "end to end asr free keyword search from speech"
    - "max pooling loss training of long short term memory networks for small footprint keyword spotting"
    - "personalized speech recognition on mobile devices"
    - "an end to end architecture for keyword spotting and voice activity detection"
    - "neural speech recognizer acoustic to word lstm model for large vocabulary speech recognition"
    - "fast and accurate recurrent neural network acoustic models for speech recognition"

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)