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: "resiliency of deep neural networks under quantization"
  Abstract: The complexity of deep neural network algorithms for hardware implementation can be much lowered by optimizing the word-length of weights and signals. Direct quantization of floating-point weights, however, does not show good performance when the number of bits assigned is small. Retraining of quantized networks has been developed to relieve this problem. In this work, the effects of retraining are analyzed for a feedforward deep neural network (FFDNN) and a convolutional neural network (CNN). The network complexity is controlled to know their effects on the resiliency of quantized networks by retraining. The complexity of the FFDNN is controlled by varying the unit size in each hidden layer and the number of layers, while that of the CNN is done by modifying the feature map configuration. We find that the performance gap between the floating-point and the retrain-based ternary (+1, 0, -1) weight neural networks exists with a fair amount in 'complexity limited' networks, but the discrepancy almost vanishes in fully complex networks whose capability is limited by the training data, rather than by the number of connections. This research shows that highly complex DNNs have the capability of absorbing the effects of severe weight quantization through retraining, but connection limited networks are less resilient. This paper also presents the effective compression ratio to guide the trade-off between the network size and the precision when the hardware resource is limited.

Connected through hyperedge HE-1 (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"
    - "same same but different recovering neural network quantization error through weight factorization"
    - "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 "generative low bitwidth data free quantization"):
  with members:
    - "zeroq a novel zero shot quantization framework"
    - "data free quantization through weight equalization and bias correction"
    - "effective training of convolutional neural networks with low bitwidth weights and activations"
    - "zero shot knowledge transfer via adversarial belief matching"
    - "improving neural network quantization without retraining using outlier channel splitting"
    - "data free learning of student networks"
    - "nat neural architecture transformer for accurate and compact architectures"
    - "zero shot knowledge distillation in deep networks"

Connected through hyperedge HE-3 (induced by source paper "sniff reverse engineering of neural networks with fault attacks"):
  with members:
    - "high fidelity extraction of neural network models"
    - "terminal brain damage exposing the graceless degradation in deep neural networks under hardware fault attacks"
    - "a simple explanation for the existence of adversarial examples with small hamming distance"
    - "stealing hyperparameters in machine learning"
    - "learning transferable architectures for scalable image recognition"
    - "aggregated residual transformations for deep neural networks"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "densely connected convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "qutibench benchmarking neural networks on heterogeneous hardware"):
  with members:
    - "analysis of dawnbench a time to accuracy machine learning performance benchmark"
    - "binareye an always on energy accuracy scalable binary cnn processor with all memory on chip in 28nm cmos"
    - "training and inference with integers in deep neural networks"
    - "tvm end to end optimization stack for deep learning"
    - "finn l library extensions and design trade off analysis for variable precision lstm networks on fpgas"
    - "bismo a scalable bit serial matrix multiplication overlay for reconfigurable computing"
    - "tbd benchmarking and analyzing deep neural network training"
    - "in datacenter performance analysis of a tensor processing unit"

Connected through hyperedge HE-5 (induced by source paper "qkd quantization aware knowledge distillation"):
  with members:
    - "data free quantization through weight equalization and bias correction"
    - "trained quantization thresholds for accurate and efficient fixed point inference of deep neural networks"
    - "differentiable quantization of deep neural networks"
    - "differentiable soft quantization bridging full precision and low bit neural networks"
    - "a comprehensive overhaul of feature distillation"
    - "feature fusion for online mutual knowledge distillation"
    - "efficient 8 bit quantization of transformer neural machine language translation model"
    - "efficientnet rethinking model scaling for convolutional neural networks"

Connected through hyperedge HE-6 (induced by source paper "fpga based accelerators of deep learning networks for learning and classification a review"):
  with members:
    - "dla compiler and fpga overlay for neural network inference acceleration"
    - "deep learning for sentiment analysis a survey"
    - "deep learning based multiple object visual tracking on embedded system for iot and mobile edge computing applications"
    - "hardware for machine learning challenges and opportunities"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "finn a framework for fast scalable binarized neural network inference"
    - "parallel multi channel convolution using general matrix multiplication"

Connected through hyperedge HE-7 (induced by source paper "learned step size quantization"):
  with members:
    - "bridging the accuracy gap for 2 bit quantized neural networks qnn"
    - "model compression via distillation and quantization"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"
    - "learning low precision deep neural networks through regularization"
    - "learning to quantize deep networks by optimizing quantization intervals with task loss"
    - "nice noise injection and clamping estimation for neural network quantization"
    - "discovering low precision networks close to full precision networks for efficient embedded inference"
    - "pact parameterized clipping activation for quantized neural networks"

Connected through hyperedge HE-8 (induced by source paper "training high performance and large scale deep neural networks with full 8 bit integers"):
  with members:
    - "scalable methods for 8 bit training of neural networks"
    - "mixed precision training of convolutional neural networks using integer operations"
    - "training and inference with integers in deep neural networks"
    - "per tensor fixed point quantization of the back propagation algorithm"
    - "learning low precision deep neural networks through regularization"
    - "training deep neural networks with 8 bit floating point numbers"
    - "balanced quantization an effective and efficient approach to quantized neural networks"
    - "extremely low bit neural network squeeze the last bit out with admm"

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)