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: "compression of deep neural networks on the fly"
  Abstract: Thanks to their state-of-the-art performance, deep neural networks are increasingly used for object recognition. To achieve these results, they use millions of parameters to be trained. However, when targeting embedded applications the size of these models becomes problematic. As a consequence, their usage on smartphones or other resource limited devices is prohibited. In this paper we introduce a novel compression method for deep neural networks that is performed during the learning phase. It consists in adding an extra regularization term to the cost function of fully-connected layers. We combine this method with Product Quantization (PQ) of the trained weights for higher savings in storage consumption. We evaluate our method on two data sets (MNIST and CIFAR10), on which we achieve significantly larger compression rates than state-of-the-art methods.

Connected through hyperedge HE-1 (induced by source paper "attention based pruning for shift networks"):
  with members:
    - "synetgy algorithm hardware co design for convnet accelerators on embedded fpgas"
    - "learning to prune filters in convolutional neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "quantized guided pruning for efficient hardware implementations of convolutional neural networks"
    - "deep k means re training and parameter sharing with harder cluster assignments for compressing deep convolutions"
    - "constructing fast network through deconstruction of convolution"
    - "soft filter pruning for accelerating deep convolutional neural networks"
    - "nisp pruning networks using neuron importance score propagation"

Connected through hyperedge HE-2 (induced by source paper "efficient hardware implementation of incremental learning and inference on chip"):
  with members:
    - "learning to prune filters in convolutional neural networks"
    - "constructing fast network through deconstruction of convolution"
    - "transfer incremental learning using data augmentation"
    - "shift a zero flop zero parameter alternative to spatial convolutions"
    - "towards the limit of network quantization"
    - "icarl incremental classifier and representation learning"
    - "deep learning on fpgas past present and future"
    - "binaryconnect training deep neural networks with binary weights during propagations"

Connected through hyperedge HE-3 (induced by source paper "training modern deep neural networks for memory fault robustness"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "a study of deep learning robustness against computation failures"
    - "squeeze and excitation networks"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "identity mappings in deep residual networks"
    - "loss aware binarization of deep networks"
    - "binaryconnect training deep neural networks with binary weights during propagations"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"

Connected through hyperedge HE-4 (induced by source paper "quantized guided pruning for efficient hardware implementations of convolutional neural networks"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "dorefa net training low bitwidth convolutional neural networks with low bitwidth gradients"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "ternary weight networks"
    - "densely connected convolutional networks"
    - "sparsely connected neural networks towards efficient vlsi implementation of deep neural networks"
    - "loss aware binarization of deep networks"

Connected through hyperedge HE-5 (induced by source paper "more is less a more complicated network with less inference complexity"):
  with members:
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "dorefa net training low bitwidth convolutional neural networks with low bitwidth gradients"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "identity mappings in deep residual networks"
    - "wide residual networks"
    - "quantized neural networks training neural networks with low precision weights and activations"
    - "pruning filters for efficient convnets"
    - "binaryconnect training deep neural networks with binary weights during propagations"

Connected through hyperedge HE-6 (induced by source paper "origami a 803 gop s w convolutional network accelerator"):
  with members:
    - "computing the stereo matching cost with a convolutional neural network"
    - "flownet learning optical flow with convolutional networks"
    - "deep residual learning for image recognition"
    - "maxdnn an efficient convolution kernel for deep learning with maxwell gpus"
    - "deep learning with limited numerical precision"
    - "epicflow edge preserving interpolation of correspondences for optical flow"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-7 (induced by source paper "reducing the model order of deep neural networks using information theory"):
  with members:
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "learning both weights and connections for efficient neural networks"
    - "deep learning with limited numerical precision"
    - "an exploration of parameter redundancy in deep networks with circulant projections"
    - "a primer on neural network models for natural language processing"
    - "adam a method for stochastic optimization"
    - "exploiting linear structure within convolutional networks for efficient evaluation"
    - "going deeper with convolutions"

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)