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: "alternating multi bit quantization for recurrent neural networks"
  Abstract: Recurrent neural networks have achieved excellent performance in many applications. However, on portable devices with limited resources, the models are often too large to deploy. For applications on the server with large scale concurrent requests, the latency during inference can also be very critical for costly computing resources. In this work, we address these problems by quantizing the network, both weights and activations, into multiple binary codes {-1,+1}. We formulate the quantization as an optimization problem. Under the key observation that once the quantization coefficients are fixed the binary codes can be derived efficiently by binary search tree, alternating minimization is then applied. We test the quantization for two well-known RNNs, i.e., long short term memory (LSTM) and gated recurrent unit (GRU), on the language models. Compared with the full-precision counter part, by 2-bit quantization we can achieve ~16x memory saving and ~6x real inference acceleration on CPUs, with only a reasonable loss in the accuracy. By 3-bit quantization, we can achieve almost no loss in the accuracy or even surpass the original model, with ~10.5x memory saving and ~3x real inference acceleration. Both results beat the exiting quantization works with large margins. We extend our alternating quantization to image classification tasks. In both RNNs and feedforward neural networks, the method also achieves excellent performance.

Connected through hyperedge HE-1 (induced by source paper "quantized neural network inference with precision batching"):
  with members:
    - "learning fast algorithms for linear transforms using butterfly factorizations"
    - "quantized reinforcement learning quarl"
    - "adaptivfloat a floating point based data type for resilient deep learning inference"
    - "improving neural network quantization without retraining using outlier channel splitting"
    - "word2bits quantized word vectors"
    - "pact parameterized clipping activation for quantized neural networks"
    - "on the state of the art of evaluation in neural language models"
    - "efficient processing of deep neural networks a tutorial and survey"

Connected through hyperedge HE-2 (induced by source paper "structured compression by weight encryption for unstructured pruning and quantization"):
  with members:
    - "network pruning for low rank binary indexing"
    - "rethinking the smaller norm less informative assumption in channel pruning of convolution layers"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "retraining based iterative weight quantization for deep neural networks"
    - "deeptwist learning model compression via occasional weight distortion"
    - "exploring the regularity of sparse structure in convolutional neural networks"
    - "channel pruning for accelerating very deep neural networks"
    - "eie efficient inference engine on compressed deep neural network"

Connected through hyperedge HE-3 (induced by source paper "autoq automated kernel wise neural network quantization"):
  with members:
    - "optimizing bit serial matrix multiplication for reconfigurable computing"
    - "low bit quantization of neural networks for efficient inference"
    - "quantizing deep convolutional networks for efficient inference a whitepaper"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "amc automl for model compression and acceleration on mobile devices"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"
    - "haq hardware aware automated quantization with mixed precision"
    - "neural architecture search with bayesian optimisation and optimal transport"

Connected through hyperedge HE-4 (induced by source paper "recurrent neural networks an embedded computing perspective"):
  with members:
    - "deep neural network approximation for custom hardware where we ve been where we re going"
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "single stream parallelization of recurrent neural networks for low power and fast inference"
    - "effective quantization approaches for recurrent neural networks"
    - "a survey on methods and theories of quantized neural networks"
    - "finn l library extensions and design trade off analysis for variable precision lstm networks on fpgas"
    - "tensor decomposition for compressing recurrent neural network"
    - "toolflows for mapping convolutional neural networks on fpgas a survey and future directions"

Connected through hyperedge HE-5 (induced by source paper "towards efficient training for neural network quantization"):
  with members:
    - "effective training of convolutional neural networks with low bitwidth weights and activations"
    - "trained quantization thresholds for accurate and efficient fixed point inference of deep neural networks"
    - "a mean field theory of batch normalization"
    - "differentiable quantization of deep neural networks"
    - "differentiable soft quantization bridging full precision and low bit neural networks"
    - "autoqb automl for network quantization and binarization on mobile devices"
    - "fixup initialization residual learning without normalization"
    - "learned step size quantization"

Connected through hyperedge HE-6 (induced by source paper "adabits neural network quantization with adaptive bit widths"):
  with members:
    - "a survey of deep learning techniques for autonomous driving"
    - "autogan neural architecture search for generative adversarial networks"
    - "single path nas designing hardware efficient convnets in less than 4 hours"
    - "detnas backbone search for object detection"
    - "differentiable quantization of deep neural networks"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "autoqb automl for network quantization and binarization on mobile devices"
    - "once for all train one network and specialize it for efficient deployment on diverse hardware platforms"

Connected through hyperedge HE-7 (induced by source paper "q bert hessian based ultra low precision quantization of bert"):
  with members:
    - "are sixteen heads really better than one"
    - "a tensorized transformer for language modeling"
    - "lightweight and efficient neural natural language processing with quaternion networks"
    - "hawq hessian aware quantization of neural networks with mixed precision"
    - "distilling task specific knowledge from bert into simple neural networks"
    - "efficient 8 bit quantization of transformer neural machine language translation model"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "patient knowledge distillation for bert model compression"

Connected through hyperedge HE-8 (induced by source paper "adaptive loss aware quantization for multi bit networks"):
  with members:
    - "differentiable soft quantization bridging full precision and low bit neural networks"
    - "on the convergence of adam and beyond"
    - "from hashing to cnns training binaryweight networks via hashing"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"
    - "relaxed quantization for discretized neural networks"
    - "syq learning symmetric quantization for efficient deep neural networks"
    - "loss aware weight quantization of deep networks"
    - "a systematic dnn weight pruning framework using alternating direction method of multipliers"

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)