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: "same same but different recovering neural network quantization error through weight factorization"
  Abstract: Quantization of neural networks has become common practice, driven by the need for efficient implementations of deep neural networks on embedded devices. In this paper, we exploit an oft-overlooked degree of freedom in most networks - for a given layer, individual output channels can be scaled by any factor provided that the corresponding weights of the next layer are inversely scaled. Therefore, a given network has many factorizations which change the weights of the network without changing its function. We present a conceptually simple and easy to implement method that uses this property and show that proper factorizations significantly decrease the degradation caused by quantization. We show improvement on a wide variety of networks and achieve state-of-the-art degradation results for MobileNets. While our focus is on quantization, this type of factorization is applicable to other domains such as network-pruning, neural nets regularization and network interpretability.

Connected through hyperedge HE-1 (induced by source paper "zeroq a novel zero shot quantization framework"):
  with members:
    - "data free quantization through weight equalization and bias correction"
    - "hawq v2 hessian aware trace weighted quantization of neural networks"
    - "cat compression aware training for bandwidth reduction"
    - "q bert hessian based ultra low precision quantization of bert"
    - "improving neural network quantization without retraining using outlier channel splitting"
    - "the knowledge within methods for data free model compression"
    - "hawq hessian aware quantization of neural networks with mixed precision"
    - "quantizing deep convolutional networks for efficient inference a whitepaper"

Connected through hyperedge HE-2 (induced by source paper "post training quantization with multiple points mixed precision without mixed precision"):
  with members:
    - "data free quantization through weight equalization and bias correction"
    - "improving neural network quantization without retraining using outlier channel splitting"
    - "additive powers of two quantization a non uniform discretization for neural networks"
    - "loss aware post training quantization"
    - "low bit quantization of neural networks for efficient inference"
    - "hawq hessian aware quantization of neural networks with mixed precision"
    - "quantizing deep convolutional networks for efficient inference a whitepaper"
    - "mobilenetv2 inverted residuals and linear bottlenecks"

Connected through hyperedge HE-3 (induced by source paper "gradient ell_1 regularization for quantization robustness"):
  with members:
    - "data free quantization through weight equalization and bias correction"
    - "improving neural network quantization without retraining using outlier channel splitting"
    - "low bit quantization of neural networks for efficient inference"
    - "defensive quantization when efficiency meets robustness"
    - "quantizing deep convolutional networks for efficient inference a whitepaper"
    - "sparse dnns with improved adversarial robustness"
    - "the singular values of convolutional layers"
    - "relaxed quantization for discretized neural networks"

Connected through hyperedge HE-4 (induced by source paper "data free quantization through weight equalization and bias correction"):
  with members:
    - "equi normalization of neural networks"
    - "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"
    - "syq learning symmetric quantization for efficient deep neural networks"

Connected through hyperedge HE-5 (induced by source paper "feature map transform coding for energy efficient cnn inference"):
  with members:
    - "dnnvm end to end compiler leveraging heterogeneous optimizations on fpga based cnn accelerators"
    - "regularizing activation distribution for training binarized deep networks"
    - "lutnet rethinking inference in fpga soft logic"
    - "low bit quantization of neural networks for efficient inference"
    - "swalp stochastic weight averaging in low precision training"
    - "hawq hessian aware quantization of neural networks with mixed precision"
    - "faster gaze prediction with dense networks and fisher pruning"
    - "uniq uniform noise injection for the quantization of neural networks"

Connected through hyperedge HE-6 (induced by source paper "fighting quantization bias with bias"):
  with members:
    - "improving neural network quantization without retraining using outlier channel splitting"
    - "understanding straight through estimator in training activation quantized neural nets"
    - "low bit quantization of neural networks for efficient inference"
    - "quantizing deep convolutional networks for efficient inference a whitepaper"
    - "uniq uniform noise injection for the quantization of neural networks"
    - "a quantization friendly separable convolution for mobilenets"
    - "model compression via distillation and quantization"
    - "quantizing convolutional neural networks for low power high throughput inference engines"

Connected through hyperedge HE-7 (induced by source paper "the knowledge within methods for data free model compression"):
  with members:
    - "data free quantization through weight equalization and bias correction"
    - "zero shot knowledge distillation in deep networks"
    - "how good is my gan"
    - "quantizing deep convolutional networks for efficient inference a whitepaper"
    - "discovering low precision networks close to full precision networks for efficient embedded inference"
    - "augment your batch better training with larger batches"
    - "large scale gan training for high fidelity natural image synthesis"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"

Connected through hyperedge HE-8 (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"
    - "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"
    - "bert pre training of deep bidirectional transformers for language understanding"

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)