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: "loss aware binarization of deep networks"
  Abstract: Deep neural network models, though very powerful and highly successful, are computationally expensive in terms of space and time. Recently, there have been a number of attempts on binarizing the network weights and activations. This greatly reduces the network size, and replaces the underlying multiplications to additions or even XNOR bit operations. However, existing binarization schemes are based on simple matrix approximation and ignore the effect of binarization on the loss. In this paper, we propose a proximal Newton algorithm with diagonal Hessian approximation that directly minimizes the loss w.r.t. the binarized weights. The underlying proximal step has an efficient closed-form solution, and the second-order information can be efficiently obtained from the second moments already computed by the Adam optimizer. Experiments on both feedforward and recurrent networks show that the proposed loss-aware binarization algorithm outperforms existing binarization schemes, and is also more robust for wide and deep networks.

Connected through hyperedge HE-1 (induced by source paper "sparse weight activation training"):
  with members:
    - "improving neural network quantization without retraining using outlier channel splitting"
    - "swalp stochastic weight averaging in low precision training"
    - "scalable methods for 8 bit training of neural networks"
    - "mixed precision training of convolutional neural networks using integer operations"
    - "bridging the accuracy gap for 2 bit quantized neural networks qnn"
    - "training and inference with integers in deep neural networks"
    - "high accuracy low precision training"
    - "per tensor fixed point quantization of the back propagation algorithm"

Connected through hyperedge HE-2 (induced by source paper "forward and backward information retention for accurate binary neural networks"):
  with members:
    - "circulant binary convolutional networks enhancing the performance of 1 bit dcnns with circulant back propagation"
    - "improved training of binary networks for human pose estimation and image recognition"
    - "libra r cnn towards balanced learning for object detection"
    - "bayesian optimized 1 bit cnns"
    - "regularizing activation distribution for training binarized deep networks"
    - "dynamic curriculum learning for imbalanced data classification"
    - "xnor net improved binary neural networks"
    - "differentiable soft quantization bridging full precision and low bit neural networks"

Connected through hyperedge HE-3 (induced by source paper "sqwa stochastic quantized weight averaging for improving the generalization capability of low precision deep neural networks"):
  with members:
    - "weight normalization based quantization for deep neural network compression"
    - "knowledge distillation for optimization of quantized deep neural networks"
    - "quantization networks"
    - "swalp stochastic weight averaging in low precision training"
    - "empirical analysis of knowledge distillation technique for optimization of quantized deep neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "model compression via distillation and quantization"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"

Connected through hyperedge HE-4 (induced by source paper "optimal gradient quantization condition for communication efficient distributed training"):
  with members:
    - "error feedback fixes signsgd and other gradient compression schemes"
    - "federated learning strategies for improving communication efficiency"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"
    - "loss aware weight quantization of deep networks"
    - "sparsified sgd with memory"
    - "signsgd compressed optimisation for non convex problems"
    - "error compensated quantized sgd and its applications to large scale distributed optimization"
    - "deep gradient compression reducing the communication bandwidth for distributed training"

Connected through hyperedge HE-5 (induced by source paper "gradient based deep quantization of neural networks through sinusoidal adaptive regularization"):
  with members:
    - "divide and conquer leveraging intermediate feature representations for quantized training of neural networks"
    - "differentiable soft quantization bridging full precision and low bit neural networks"
    - "mixed precision dnns all you need is a good parametrization"
    - "sinreq generalized sinusoidal regularization for low bitwidth deep quantized training"
    - "learned step size quantization"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"
    - "learning low precision deep neural networks through regularization"
    - "loss aware weight quantization of deep networks"

Connected through hyperedge HE-6 (induced by source paper "effective training of convolutional neural networks with low bitwidth weights and activations"):
  with members:
    - "knowledge adaptation for efficient semantic segmentation"
    - "single path one shot neural architecture search with uniform sampling"
    - "precision highway for ultra low precision quantization"
    - "amc automl for model compression and acceleration on mobile devices"
    - "bi real net enhancing the performance of 1 bit cnns with improved representational capability and advanced training algorithm"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"
    - "learning to quantize deep networks by optimizing quantization intervals with task loss"
    - "proxylessnas direct neural architecture search on target task and hardware"

Connected through hyperedge HE-7 (induced by source paper "training quantized neural networks with the full precision auxiliary module"):
  with members:
    - "effective training of convolutional neural networks with low bitwidth weights and activations"
    - "circulant binary convolutional networks enhancing the performance of 1 bit dcnns with circulant back propagation"
    - "relational knowledge distillation"
    - "bi real net enhancing the performance of 1 bit cnns with improved representational capability and advanced training algorithm"
    - "yolov3 an incremental improvement"
    - "proxquant quantized neural networks via proximal operators"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"
    - "self supervised knowledge distillation using singular value decomposition"

Connected through hyperedge HE-8 (induced by source paper "efficient neural architecture search via proximal iterations"):
  with members:
    - "adaptive stochastic natural gradient method for one shot neural architecture search"
    - "searching for a robust neural architecture in four gpu hours"
    - "single path one shot neural architecture search with uniform sampling"
    - "bayesnas a bayesian approach for neural architecture search"
    - "amc automl for model compression and acceleration on mobile devices"
    - "regularized evolution for image classifier architecture search"
    - "proxquant quantized neural networks via proximal operators"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"

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.NE(Neural and Evolutionary Computing)