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: "skip connections as effective symmetry breaking"
  Abstract: Skip connections made the training of very deep neural networks possible and have become an indispendable component in a variety of neural architectures. A completely satisfactory explanation for their success remains elusive. Here, we present a novel explanation for the benefits of skip connections in training very deep neural networks. We argue that skip connections help break symmetries inherent in the loss landscapes of deep networks, leading to drastically simplified landscapes. In particular, skip connections between adjacent layers in a multilayer network break the permutation symmetry of nodes in a given layer, and the recently proposed DenseNet architecture, where each layer projects skip connections to every layer above it, also breaks the rescaling symmetry of connectivity matrices between different layers. This hypothesis is supported by evidence from a toy model with binary weights and from experiments with fully-connected networks suggesting (i) that skip connections do not necessarily improve training unless they help break symmetries and (ii) that alternative ways of breaking the symmetries also lead to significant performance improvements in training deep networks, hence there is nothing special about skip connections in this respect. We find, however, that skip connections confer additional benefits over and above symmetry-breaking, such as the ability to deal effectively with the vanishing gradients problem.

Connected through hyperedge HE-1 (induced by source paper "ap mtl attention pruned multi task learning model for real time instrument detection and segmentation in robot assisted surgery"):
  with members:
    - "real time joint object detection and semantic segmentation network for automated driving"
    - "learning where to look while tracking instruments in robot assisted surgery"
    - "2017 robotic instrument segmentation challenge"
    - "visualizing uncertainty and saliency maps of deep convolutional neural networks for medical imaging applications"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "yolov3 an incremental improvement"
    - "automatic instrument segmentation in robot assisted surgery using deep learning"
    - "ternausnet u net with vgg11 encoder pre trained on imagenet for image segmentation"

Connected through hyperedge HE-2 (induced by source paper "multi task learning for automotive foggy scene understanding via domain adaptation to an illumination invariant representation"):
  with members:
    - "curriculum model adaptation with synthetic and real data for semantic foggy scene understanding"
    - "model adaptation with synthetic and real data for semantic dense foggy scene understanding"
    - "learning to adapt structured output space for semantic segmentation"
    - "semantic foggy scene understanding with synthetic data"
    - "incorporating luminance depth and color information by fusion based networks for semantic segmentation"
    - "skip connections eliminate singularities"
    - "demystifying neural style transfer"
    - "fast and accurate image super resolution by deep cnn with skip connection and network in network"

Connected through hyperedge HE-3 (induced by source paper "environment sound classification using multiple feature channels and attention based deep convolutional neural network"):
  with members:
    - "end to end environmental sound classification using a 1d convolutional neural network"
    - "multi stream network with temporal attention for environmental sound classification"
    - "randomly weighted cnns for music audio classification"
    - "study of residual networks for image recognition"
    - "timbretron a wavenet cyclegan cqt audio pipeline for musical timbre transfer"
    - "deep convolutional neural network with mixup for environmental sound classification"
    - "comparison of time frequency representations for environmental sound classification using convolutional neural networks"
    - "learning from between class examples for deep sound recognition"

Connected through hyperedge HE-4 (induced by source paper "select attend and transfer light learnable skip connections"):
  with members:
    - "iamnn iterative and adaptive mobile neural network for efficient image classification"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "squeeze and excitation networks"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "residual attention network for image classification"
    - "channel pruning for accelerating very deep neural networks"
    - "learning structured sparsity in deep neural networks"

Connected through hyperedge HE-5 (induced by source paper "training deeper neural machine translation models with transparent attention"):
  with members:
    - "deep contextualized word representations"
    - "the best of both worlds combining recent advances in neural machine translation"
    - "attention is all you need"
    - "sharp models on dull hardware fast and accurate neural machine translation decoding on the cpu"
    - "massive exploration of neural machine translation architectures"
    - "convolutional sequence to sequence learning"
    - "deep neural machine translation with linear associative unit"
    - "deep architectures for neural machine translation"

Connected through hyperedge HE-6 (induced by source paper "compnet neural networks growing via the compact network morphism"):
  with members:
    - "on weight initialization in deep neural networks"
    - "an equivalence of fully connected layer and convolutional layer"
    - "neural optimizer search with reinforcement learning"
    - "machine learning modeling for time series problem predicting flight ticket prices"
    - "a survey of model compression and acceleration for deep neural networks"
    - "channel pruning for accelerating very deep neural networks"
    - "deep residual learning for image recognition"
    - "net2net accelerating learning via knowledge transfer"

Connected through hyperedge HE-7 (induced by source paper "directional analysis of stochastic gradient descent via von mises fisher distributions in deep learning"):
  with members:
    - "don t decay the learning rate increase the batch size"
    - "opening the black box of deep neural networks via information"
    - "how to escape saddle points efficiently"
    - "on large batch training for deep learning generalization gap and sharp minima"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "optimizing neural networks with kronecker factored approximate curvature"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

Connected through hyperedge HE-8 (induced by source paper "cnn based projected gradient descent for consistent ct image reconstruction"):
  with members:
    - "efficient sum of outer products dictionary learning soup dil and its application to inverse problems"
    - "deep learning with domain adaptation for accelerated projection reconstruction mr"
    - "one network to solve them all solving linear inverse problems using deep projection models"
    - "deep learning for photoacoustic tomography from sparse data"
    - "recurrent inference machines for solving inverse problems"
    - "the little engine that could regularization by denoising red"
    - "plug and play admm for image restoration fixed point convergence and applications"

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)