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: "eraserelu a simple way to ease the training of deep convolution neural networks"
  Abstract: For most state-of-the-art architectures, Rectified Linear Unit (ReLU) becomes a standard component accompanied with each layer. Although ReLU can ease the network training to an extent, the character of blocking negative values may suppress the propagation of useful information and leads to the difficulty of optimizing very deep Convolutional Neural Networks (CNNs). Moreover, stacking layers with nonlinear activations is hard to approximate the intrinsic linear transformations between feature representations. #R##N#In this paper, we investigate the effect of erasing ReLUs of certain layers and apply it to various representative architectures following deterministic rules. It can ease the optimization and improve the generalization performance for very deep CNN models. We find two key factors being essential to the performance improvement: 1) the location where ReLU should be erased inside the basic module; 2) the proportion of basic modules to erase ReLU; We show that erasing the last ReLU layer of all basic modules in a network usually yields improved performance. In experiments, our approach successfully improves the performance of various representative architectures, and we report the improved results on SVHN, CIFAR-10/100, and ImageNet. Moreover, we achieve competitive single-model performance on CIFAR-100 with 16.53% error rate compared to state-of-the-art.

Connected through hyperedge HE-1 (induced by source paper "crypto oriented neural architecture design"):
  with members:
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "xonn xnor based oblivious deep neural network inference"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "security and privacy issues in deep learning"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "chameleon a hybrid secure computation framework for machine learning applications"
    - "tapas tricks to accelerate encrypted prediction as a service"
    - "gazelle a low latency framework for secure neural network inference"

Connected through hyperedge HE-2 (induced by source paper "learning to play the chess variant crazyhouse above world champion level with deep neural networks and human data"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "a disciplined approach to neural network hyper parameters part 1 learning rate batch size momentum and weight decay"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "rethink relu to training better cnns"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "super convergence very fast training of residual networks using large learning rates"
    - "improving generalization performance by switching from adam to sgd"
    - "systematic evaluation of convolution neural network advances on the imagenet"

Connected through hyperedge HE-3 (induced by source paper "snapshot distillation teacher student optimization in one generation"):
  with members:
    - "knowledge distillation in generations more tolerant teachers educate better students"
    - "label refinery improving imagenet classification through label progression"
    - "on calibration of modern neural networks"
    - "progressive neural architecture search"
    - "random erasing data augmentation"
    - "deep mutual learning"
    - "snapshot ensembles train 1 get m for free"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"

Connected through hyperedge HE-4 (induced by source paper "shakedrop regularization for deep residual learning"):
  with members:
    - "an alternative view when does sgd escape local minima"
    - "mixup beyond empirical risk minimization"
    - "between class learning for image classification"
    - "improved regularization of convolutional neural networks with cutout"
    - "shake shake regularization"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "aggregated residual transformations for deep neural networks"
    - "deep networks with stochastic depth"

Connected through hyperedge HE-5 (induced by source paper "norm preservation why residual networks can become extremely deep"):
  with members:
    - "the singular values of convolutional layers"
    - "implicit bias of gradient descent on linear convolutional networks"
    - "representing smooth functions as compositions of near identity functions with implications for deep network optimization"
    - "are resnets provably better than linear predictors"
    - "skip connections eliminate singularities"
    - "the reversible residual network backpropagation without storing activations"
    - "the shattered gradients problem if resnets are the answer then what is the question"
    - "density estimation using real nvp"

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)