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: "retraining based iterative weight quantization for deep neural networks"
  Abstract: Model compression has gained a lot of attention due to its ability to reduce hardware resource requirements significantly while maintaining accuracy of DNNs. Model compression is especially useful for memory-intensive recurrent neural networks because smaller memory footprint is crucial not only for reducing storage requirement but also for fast inference operations. Quantization is known to be an effective model compression method and researchers are interested in minimizing the number of bits to represent parameters. In this work, we introduce an iterative technique to apply quantization, presenting high compression ratio without any modifications to the training algorithm. In the proposed technique, weight quantization is followed by retraining the model with full precision weights. We show that iterative retraining generates new sets of weights which can be quantized with decreasing quantization loss at each iteration. We also show that quantization is efficiently able to leverage pruning, another effective model compression method. Implementation issues on combining the two methods are also addressed. Our experimental results demonstrate that an LSTM model using 1-bit quantized weights is sufficient for PTB dataset without any accuracy degradation while previous methods demand at least 2-4 bits for quantized weights.

Connected through hyperedge HE-1 (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"
    - "alternating multi bit quantization for recurrent neural networks"
    - "the lottery ticket hypothesis finding sparse trainable 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-2 (induced by source paper "compressing language models using doped kronecker products"):
  with members:
    - "compression of recurrent neural networks for efficient language modeling"
    - "compressing rnns for iot devices by 15 38x using kronecker products"
    - "run time efficient rnn compression for inference on edge devices"
    - "pushing the limits of rnn compression"
    - "measuring scheduling efficiency of rnns for nlp applications"
    - "deeptwist learning model compression via occasional weight distortion"
    - "factorization tricks for lstm networks"
    - "compressing recurrent neural network with tensor train"

Connected through hyperedge HE-3 (induced by source paper "waldorf wasteless language model distillation on reading comprehension"):
  with members:
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "analyzing multi head self attention specialized heads do the heavy lifting the rest can be pruned"
    - "distilbert a distilled version of bert smaller faster cheaper and lighter"
    - "bert rediscovers the classical nlp pipeline"
    - "megatron lm training multi billion parameter language models using model parallelism"
    - "do nlp models know numbers probing numeracy in embeddings"
    - "spanbert improving pre training by representing and predicting spans"
    - "albert a lite bert for self supervised learning of language representations"

Connected through hyperedge HE-4 (induced by source paper "learning low rank approximation for cnns"):
  with members:
    - "model compression via distillation and quantization"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "deeptwist learning model compression via occasional weight distortion"
    - "groupreduce block wise low rank approximation for neural language model shrinking"
    - "in datacenter performance analysis of a tensor processing unit"
    - "mec memory efficient convolution for deep neural network"
    - "tensorly tensor learning in python"
    - "learning both weights and connections for efficient neural networks"

Connected through hyperedge HE-5 (induced by source paper "antman sparse low rank compression to accelerate rnn inference"):
  with members:
    - "slimnets an exploration of deep model compression and acceleration"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "learning intrinsic sparse structures within long short term memory"
    - "regularizing and optimizing lstm language models"
    - "exploring sparsity in recurrent neural networks"
    - "a survey of model compression and acceleration for deep neural networks"
    - "learning compact recurrent neural networks with block term tensor decomposition"
    - "learning structured sparsity in deep neural networks"

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)