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: "fixed point performance analysis of recurrent neural networks"
  Abstract: Recurrent neural networks have shown excellent performance in many applications, however they require increased complexity in hardware or software based implementations. The hardware complexity can be much lowered by minimizing the word-length of weights and signals. This work analyzes the fixed-point performance of recurrent neural networks using a retrain based quantization method. The quantization sensitivity of each layer in RNNs is studied, and the overall fixed-point optimization results minimizing the capacity of weights while not sacrificing the performance are presented. A language model and a phoneme recognition examples are used.

Connected through hyperedge HE-1 (induced by source paper "improving neural network quantization without retraining using outlier channel splitting"):
  with members:
    - "bridging the accuracy gap for 2 bit quantized neural networks qnn"
    - "quantizing convolutional neural networks for low power high throughput inference engines"
    - "discovering low precision networks close to full precision networks for efficient embedded inference"
    - "pact parameterized clipping activation for quantized neural networks"
    - "value aware quantization for training and inference of neural networks"
    - "post training 4 bit quantization of convolution networks for rapid deployment"
    - "towards effective low bitwidth convolutional neural networks"
    - "incremental network quantization towards lossless cnns with low precision weights"

Connected through hyperedge HE-2 (induced by source paper "post training 4 bit quantization on embedding tables"):
  with members:
    - "on the downstream performance of compressed word embeddings"
    - "deep learning recommendation model for personalization and recommendation systems"
    - "improving neural network quantization without retraining using outlier channel splitting"
    - "low bit quantization of neural networks for efficient inference"
    - "quantizing deep convolutional networks for efficient inference a whitepaper"
    - "high accuracy low precision training"
    - "deep learning inference in facebook data centers characterization performance optimizations and hardware implications"
    - "post training 4 bit quantization of convolution networks for rapid deployment"

Connected through hyperedge HE-3 (induced by source paper "overwrite quantization opportunistic outlier handling for neural network accelerators"):
  with members:
    - "data free quantization through weight equalization and bias correction"
    - "synetgy algorithm hardware co design for convnet accelerators on embedded fpgas"
    - "improving neural network quantization without retraining using outlier channel splitting"
    - "same same but different recovering neural network quantization error through weight factorization"
    - "energy and policy considerations for deep learning in nlp"
    - "scalable methods for 8 bit training of neural networks"
    - "dnn dataflow choice is overrated"
    - "discovering low precision networks close to full precision networks for efficient embedded inference"

Connected through hyperedge HE-4 (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-5 (induced by source paper "fixed point optimization of deep neural networks with adaptive step size retraining"):
  with members:
    - "scaling binarized neural networks on reconfigurable logic"
    - "fpga based low power speech recognition with recurrent neural networks"
    - "eie efficient inference engine on compressed deep neural network"
    - "fpga based implementation of deep neural networks using on chip memory only"
    - "resiliency of deep neural networks under quantization"
    - "binaryconnect training deep neural networks with binary weights during propagations"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "fixed point quantization of deep convolutional networks"

Connected through hyperedge HE-6 (induced by source paper "convolutional neural networks using logarithmic data representation"):
  with members:
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "resiliency of deep neural networks under quantization"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "tensorizing neural networks"
    - "learning both weights and connections for efficient neural networks"
    - "neural networks with few multiplications"
    - "deep residual learning for image recognition"
    - "deep learning with limited numerical precision"

Connected through hyperedge HE-7 (induced by source paper "quantized neural network design under weight capacity constraint"):
  with members:
    - "dynamic hand gesture recognition for wearable devices with low complexity recurrent neural networks"
    - "xnor net imagenet classification using binary convolutional neural networks"
    - "binaryconnect training deep neural networks with binary weights during propagations"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "fast and accurate recurrent neural network acoustic models for speech recognition"
    - "structured pruning of deep convolutional neural networks"
    - "deepdriving learning affordance for direct perception in autonomous driving"

Connected through hyperedge HE-8 (induced by source paper "fpga based low power speech recognition with recurrent neural networks"):
  with members:
    - "eie efficient inference engine on compressed deep neural network"
    - "character level incremental speech recognition with recurrent neural networks"
    - "fpga based implementation of deep neural networks using on chip memory only"
    - "single stream parallelization of generalized lstm like rnns on a gpu"
    - "deep speech 2 end to end speech recognition in english and mandarin"
    - "eesen end to end speech recognition using deep rnn models and wfst based decoding"
    - "adadelta an adaptive learning rate method"

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)