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: "big neural networks waste capacity"
  Abstract: This article exposes the failure of some big neural networks to leverage added capacity to reduce underfitting. Past research suggest diminishing returns when increasing the size of neural networks. Our experiments on ImageNet LSVRC-2010 show that this may be due to the fact there are highly diminishing returns for capacity in terms of training error, leading to underfitting. This suggests that the optimization method - first order gradient descent - fails at this regime. Directly attacking this problem, either through the optimization method or the choices of parametrization, may allow to improve the generalization error on large datasets, for which a large capacity is required.

Connected through hyperedge HE-1 (induced by source paper "full deep neural network training on a pruned weight budget"):
  with members:
    - "measuring the intrinsic dimension of objective landscapes"
    - "revisiting small batch training for deep neural networks"
    - "learning efficient convolutional networks through network slimming"
    - "cnvlutin2 ineffectual activation and weight free deep neural network computing"
    - "domain adaptive deep network compression"
    - "training quantized nets a deeper understanding"
    - "thinet a filter level pruning method for deep neural network compression"
    - "deep learning with low precision by half wave gaussian quantization"

Connected through hyperedge HE-2 (induced by source paper "deconstructing lottery tickets zeros signs and the supermask"):
  with members:
    - "measuring the intrinsic dimension of objective landscapes"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "piggyback adapting a single network to multiple tasks by learning to mask weights"
    - "thinet a filter level pruning method for deep neural network compression"
    - "learning structured sparsity in deep neural networks"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "pruning convolutional neural networks for resource efficient inference"
    - "pruning filters for efficient convnets"

Connected through hyperedge HE-3 (induced by source paper "quantization loss re learning method"):
  with members:
    - "towards binary valued gates for robust lstm training"
    - "optimal hyperparameters for deep lstm networks for sequence labeling tasks"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "bitwise neural networks"
    - "the concrete distribution a continuous relaxation of discrete random variables"
    - "binaryconnect training deep neural networks with binary weights during propagations"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "bidirectional lstm crf models for sequence tagging"

Connected through hyperedge HE-4 (induced by source paper "data augmentation instead of explicit regularization"):
  with members:
    - "regularization for deep learning a taxonomy"
    - "further advantages of data augmentation on convolutional neural networks"
    - "mixup as locally linear out of manifold regularization"
    - "do deep nets really need weight decay and dropout"
    - "smart augmentation learning an optimal data augmentation strategy"
    - "the effectiveness of data augmentation in image classification using deep learning"
    - "spectrally normalized margin bounds for neural networks"
    - "improved regularization of convolutional neural networks with cutout"

Connected through hyperedge HE-5 (induced by source paper "secs efficient deep stream processing via class skew dichotomy"):
  with members:
    - "ucnn exploiting computational reuse in deep neural networks via weight repetition"
    - "eva 2 exploiting temporal redundancy in live computer vision"
    - "core50 a new dataset and benchmark for continuous object recognition"
    - "branchynet fast inference via early exiting from deep neural networks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "lcnn lookup based convolutional neural network"
    - "densely connected convolutional networks"

Connected through hyperedge HE-6 (induced by source paper "xnor net imagenet classification using binary convolutional neural networks"):
  with members:
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "bitwise neural networks"
    - "binaryconnect training deep neural networks with binary weights during propagations"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "learning both weights and connections for efficient neural networks"
    - "neural networks with few multiplications"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-7 (induced by source paper "the sp theory of intelligence distinctive features and advantages"):
  with members:
    - "semantic amodal segmentation"
    - "deep learning in neural networks"
    - "proposal for the creation of a research facility for the development of the sp machine"
    - "text understanding from scratch"
    - "sketch a net that beats humans"
    - "deep neural networks are easily fooled high confidence predictions for unrecognizable images"
    - "neural turing machines"
    - "application of the sp theory of intelligence to the understanding of natural vision and the development of computer vision"

Connected through hyperedge HE-8 (induced by source paper "blending lstms into cnns"):
  with members:
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "knowledge transfer pre training"
    - "deep residual learning for image recognition"
    - "transferring knowledge from a rnn to a dnn"
    - "training very deep networks"
    - "fitnets hints for thin deep nets"
    - "very deep convolutional networks for large scale image recognition"
    - "long short term memory based recurrent neural network architectures for large vocabulary speech recognition"

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)