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: "flattened convolutional neural networks for feedforward acceleration"
  Abstract: We present flattened convolutional neural networks that are designed for fast feedforward execution. The redundancy of the parameters, especially weights of the convolutional filters in convolutional neural networks has been extensively studied and different heuristics have been proposed to construct a low rank basis of the filters after training. In this work, we train flattened networks that consist of consecutive sequence of one-dimensional filters across all directions in 3D space to obtain comparable performance as conventional convolutional networks. We tested flattened model on different datasets and found that the flattened layer can effectively substitute for the 3D filters without loss of accuracy. The flattened convolution pipelines provide around two times speed-up during feedforward pass compared to the baseline model due to the significant reduction of learning parameters. Furthermore, the proposed method does not require efforts in manual tuning or post processing once the model is trained.

Connected through hyperedge HE-1 (induced by source paper "xsepconv extremely separated convolution"):
  with members:
    - "convolution with even sized kernels and symmetric padding"
    - "mixconv mixed depthwise convolutional kernels"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "igcv3 interleaved low rank group convolutions for efficient deep neural networks"
    - "bag of tricks for image classification with convolutional neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "regularized evolution for image classifier architecture search"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"

Connected through hyperedge HE-2 (induced by source paper "dicenet dimension wise convolutions for efficient networks"):
  with members:
    - "exploring randomly wired neural networks for image recognition"
    - "searching for mobilenetv3"
    - "mixconv mixed depthwise convolutional kernels"
    - "discovering neural wirings"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "espnet efficient spatial pyramid of dilated convolutions for semantic segmentation"
    - "tiny dsod lightweight object detection for resource restricted usages"
    - "mobilenetv2 inverted residuals and linear bottlenecks"

Connected through hyperedge HE-3 (induced by source paper "efficient differentiable neural architecture search with meta kernels"):
  with members:
    - "res2net a new multi scale backbone architecture"
    - "autogan neural architecture search for generative adversarial networks"
    - "single path nas designing hardware efficient convnets in less than 4 hours"
    - "searching for a robust neural architecture in four gpu hours"
    - "detnas backbone search for object detection"
    - "exploring randomly wired neural networks for image recognition"
    - "densely connected search space for more flexible neural architecture search"
    - "searching for mobilenetv3"

Connected through hyperedge HE-4 (induced by source paper "adversarially trained model compression when robustness meets efficiency"):
  with members:
    - "learning sparsity and quantization jointly and automatically for neural network compression via constrained optimization"
    - "adversarial robustness may be at odds with simplicity"
    - "defensive quantization when efficiency meets robustness"
    - "dual dynamic inference enabling more efficient adaptive and controllable deep inference"
    - "towards imperceptible and robust adversarial example attacks against neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "sparse dnns with improved adversarial robustness"
    - "stochastic activation pruning for robust adversarial defense"

Connected through hyperedge HE-5 (induced by source paper "model compression with adversarial robustness a unified optimization framework"):
  with members:
    - "learning sparsity and quantization jointly and automatically for neural network compression via constrained optimization"
    - "adversarial robustness may be at odds with simplicity"
    - "collaborative global local networks for memory efficient segmentation of ultra high resolution images"
    - "defensive quantization when efficiency meets robustness"
    - "dual dynamic inference enabling more efficient adaptive and controllable deep inference"
    - "towards imperceptible and robust adversarial example attacks against neural networks"
    - "sparse dnns with improved adversarial robustness"
    - "stochastic activation pruning for robust adversarial defense"

Connected through hyperedge HE-6 (induced by source paper "hbonet harmonious bottleneck on two orthogonal dimensions"):
  with members:
    - "on the convergence of adam and beyond"
    - "igcv3 interleaved low rank group convolutions for efficient deep neural networks"
    - "accelerating deep neural networks with spatial bottleneck modules"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "efficient neural architecture search via parameter sharing"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-7 (induced by source paper "expandnets linear over parameterization to train compact convolutional networks"):
  with members:
    - "acnet strengthening the kernel skeletons for powerful cnn via asymmetric convolution blocks"
    - "the impact of neural network overparameterization on gradient confusion and stochastic gradient descent"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "yolo lite a real time object detection algorithm optimized for non gpu computers"
    - "yolov3 an incremental improvement"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "learning deep representations with probabilistic knowledge transfer"

Connected through hyperedge HE-8 (induced by source paper "resource efficient 3d convolutional neural networks"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "a short note about kinetics 600"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "slowfast networks for video recognition"
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action 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.NE(Neural and Evolutionary Computing)