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: "back to simplicity how to train accurate bnns from scratch"
  Abstract: Binary Neural Networks (BNNs) show promising progress in reducing computational and memory costs but suffer from substantial accuracy degradation compared to their real-valued counterparts on large-scale datasets, e.g., ImageNet. Previous work mainly focused on reducing quantization errors of weights and activations, whereby a series of approximation methods and sophisticated training tricks have been proposed. In this work, we make several observations that challenge conventional wisdom. We revisit some commonly used techniques, such as scaling factors and custom gradients, and show that these methods are not crucial in training well-performing BNNs. On the contrary, we suggest several design principles for BNNs based on the insights learned and demonstrate that highly accurate BNNs can be trained from scratch with a simple training strategy. We propose a new BNN architecture BinaryDenseNet, which significantly surpasses all existing 1-bit CNNs on ImageNet without tricks. In our experiments, BinaryDenseNet achieves 18.6% and 7.6% relative improvement over the well-known XNOR-Network and the current state-of-the-art Bi-Real Net in terms of top-1 accuracy on ImageNet, respectively.

Connected through hyperedge HE-1 (induced by source paper "bats binary architecture search"):
  with members:
    - "learning architectures for binary networks"
    - "circulant binary convolutional networks enhancing the performance of 1 bit dcnns with circulant back propagation"
    - "regularizing activation distribution for training binarized deep networks"
    - "accurate and compact convolutional neural networks with trained binarization"
    - "xnor net improved binary neural networks"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "progressive differentiable architecture search bridging the depth gap between search and evaluation"
    - "latent weights do not exist rethinking binarized neural network optimization"

Connected through hyperedge HE-2 (induced by source paper "latent weights do not exist rethinking binarized neural network optimization"):
  with members:
    - "improved training of binary networks for human pose estimation and image recognition"
    - "regularizing activation distribution for training binarized deep networks"
    - "binary ensemble neural network more bits per network or more networks per bit"
    - "model compression via distillation and quantization"
    - "bi real net enhancing the performance of 1 bit cnns with improved representational capability and advanced training algorithm"
    - "probabilistic binary neural networks"
    - "structured binary neural networks for accurate image classification and semantic segmentation"
    - "the high dimensional geometry of binary neural networks"

Connected through hyperedge HE-3 (induced by source paper "balanced binary neural networks with gated residual"):
  with members:
    - "circulant binary convolutional networks enhancing the performance of 1 bit dcnns with circulant back propagation"
    - "dabnn a super fast inference framework for binary neural networks on arm devices"
    - "attribute aware pooling for pedestrian attribute recognition"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "searching for accurate binary neural architectures"
    - "binary ensemble neural network more bits per network or more networks per bit"
    - "projection convolutional neural networks for 1 bit cnns via discrete back propagation"
    - "bi real net enhancing the performance of 1 bit cnns with improved representational capability and advanced training algorithm"

Connected through hyperedge HE-4 (induced by source paper "loss aware post training quantization"):
  with members:
    - "binaryduo reducing gradient mismatch in binary activation network by coupling binary activations"
    - "near lossless post training quantization of deep neural networks via a piecewise linear approximation"
    - "bit efficient quantization for deep neural networks"
    - "low bit quantization of neural networks for efficient inference"
    - "fighting quantization bias with bias"
    - "towards efficient training for neural network quantization"
    - "quantizing deep convolutional networks for efficient inference a whitepaper"
    - "nice noise injection and clamping estimation for neural network quantization"

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)