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: "dropneuron simplifying the structure of deep neural networks"
  Abstract: Deep learning using multi-layer neural networks (NNs) architecture manifests superb power in modern machine learning systems. The trained Deep Neural Networks (DNNs) are typically large. The question we would like to address is whether it is possible to simplify the NN during training process to achieve a reasonable performance within an acceptable computational time. We presented a novel approach of optimising a deep neural network through regularisation of net- work architecture. We proposed regularisers which support a simple mechanism of dropping neurons during a network training process. The method supports the construction of a simpler deep neural networks with compatible performance with its simplified version. As a proof of concept, we evaluate the proposed method with examples including sparse linear regression, deep autoencoder and convolutional neural network. The valuations demonstrate excellent performance. #R##N#The code for this work can be found in this http URL

Connected through hyperedge HE-1 (induced by source paper "efficient structured pruning and architecture searching for group convolution"):
  with members:
    - "fully learnable group convolution for acceleration of deep neural networks"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "building efficient deep neural networks with unitary group convolutions"
    - "regularized evolution for image classifier architecture search"
    - "darts differentiable architecture search"
    - "extreme network compression via filter group approximation"
    - "mnasnet platform aware neural architecture search for mobile"
    - "rethinking the value of network pruning"

Connected through hyperedge HE-2 (induced by source paper "multi layer pruning framework for compressing single shot multibox detector"):
  with members:
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "thinet a filter level pruning method for deep neural network compression"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "towards lightweight convolutional neural networks for object detection"
    - "channel pruning for accelerating very deep neural networks"
    - "paying more attention to attention improving the performance of convolutional neural networks via attention transfer"
    - "ssd single shot multibox detector"
    - "doubly convolutional neural networks"

Connected through hyperedge HE-3 (induced by source paper "learning grouped convolution for efficient domain adaptation"):
  with members:
    - "fully learnable group convolution for acceleration of deep neural networks"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "building efficient deep neural networks with unitary group convolutions"
    - "darts differentiable architecture search"
    - "extreme network compression via filter group approximation"
    - "mnasnet platform aware neural architecture search for mobile"
    - "rethinking the value of network pruning"
    - "nisp pruning networks using neuron importance score propagation"

Connected through hyperedge HE-4 (induced by source paper "mpdcompress matrix permutation decomposition algorithm for deep neural network compression"):
  with members:
    - "shakeout a new approach to regularized deep neural network training"
    - "on the importance of stereo for accurate depth estimation an efficient semi supervised deep neural network approach"
    - "toward low flying autonomous mav trail navigation using deep neural networks for environmental awareness"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "deeppicar a low cost deep neural network based autonomous car"
    - "structured deep neural network pruning via matrix pivoting"
    - "sep nets small and effective pattern networks"

Connected through hyperedge HE-5 (induced by source paper "structured pruning of neural networks with budget aware regularization"):
  with members:
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "efficient progressive neural architecture search"
    - "efficient neural architecture search via parameter sharing"
    - "progressive neural architecture search"
    - "learning efficient convolutional networks through network slimming"
    - "morphnet fast simple resource constrained structure learning of deep networks"
    - "learning structured sparsity in deep neural networks"
    - "wide residual networks"

Connected through hyperedge HE-6 (induced by source paper "compressing neural networks using the variational information bottleneck"):
  with members:
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "more is less a more complicated network with less inference complexity"
    - "learning structured sparsity in deep neural networks"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "deep variational information bottleneck"
    - "binaryconnect training deep neural networks with binary weights during propagations"
    - "learning both weights and connections for efficient neural networks"

Connected through hyperedge HE-7 (induced by source paper "lightweight classification of iot malware based on image recognition"):
  with members:
    - "imbalanced malware images classification a cnn based approach"
    - "very deep convolutional networks for large scale image 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.CV(Computer Vision and Pattern Recognition)