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: "multigrid neural architectures"
  Abstract: We propose a multigrid extension of convolutional neural networks (CNNs). Rather than manipulating representations living on a single spatial grid, our network layers operate across scale space, on a pyramid of grids. They consume multigrid inputs and produce multigrid outputs; convolutional filters themselves have both within-scale and cross-scale extent. This aspect is distinct from simple multiscale designs, which only process the input at different scales. Viewed in terms of information flow, a multigrid network passes messages across a spatial pyramid. As a consequence, receptive field size grows exponentially with depth, facilitating rapid integration of context. Most critically, multigrid structure enables networks to learn internal attention and dynamic routing mechanisms, and use them to accomplish tasks on which modern CNNs fail. #R##N#Experiments demonstrate wide-ranging performance advantages of multigrid. On CIFAR and ImageNet classification tasks, flipping from a single grid to multigrid within the standard CNN paradigm improves accuracy, while being compute and parameter efficient. Multigrid is independent of other architectural choices; we show synergy in combination with residual connections. Multigrid yields dramatic improvement on a synthetic semantic segmentation dataset. Most strikingly, relatively shallow multigrid networks can learn to directly perform spatial transformation tasks, where, in contrast, current CNNs fail. Together, our results suggest that continuous evolution of features on a multigrid pyramid is a more powerful alternative to existing CNN designs on a flat grid.

Connected through hyperedge HE-1 (induced by source paper "slimconv reducing channel redundancy in convolutional neural networks by weights flipping"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "res2net a new multi scale backbone architecture"
    - "feratt facial expression recognition with attention net"
    - "efficientdet scalable and efficient object detection"
    - "selective kernel networks"
    - "universally slimmable networks and improved training techniques"
    - "autoslim towards one shot architecture search for channel numbers"
    - "efficientnet rethinking model scaling for convolutional neural networks"

Connected through hyperedge HE-2 (induced by source paper "mutualnet adaptive convnet via mutual learning from network width and resolution"):
  with members:
    - "adascale towards real time video object detection using adaptive scaling"
    - "universally slimmable networks and improved training techniques"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "fast autoaugment"
    - "deep high resolution representation learning for human pose estimation"
    - "drop an octave reducing spatial redundancy in convolutional neural networks with octave convolution"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"

Connected through hyperedge HE-3 (induced by source paper "resolution adaptive networks for efficient inference"):
  with members:
    - "convolutional networks with dense connectivity"
    - "adaptative inference cost with convolutional neural mixture models"
    - "deep high resolution representation learning for human pose estimation"
    - "model slicing for supporting complex analytics with elastic inference cost and resource constraints"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"

Connected through hyperedge HE-4 (induced by source paper "graph prolongation convolutional networks explicitly multiscale machine learning on graphs with applications to modeling of biological systems"):
  with members:
    - "novel diffusion derived distance measures for graphs"
    - "lanczosnet multi scale deep graph convolutional networks"
    - "a gentle introduction to deep learning for graphs"
    - "hierarchical graph representation learning with differentiable pooling"
    - "multilevel artificial neural network training for spatially correlated learning"
    - "tensor graph convolutional neural network"
    - "pymanopt a python toolbox for optimization on manifolds using automatic differentiation"
    - "semi supervised classification with graph convolutional networks"

Connected through hyperedge HE-5 (induced by source paper "benchmarking the robustness of semantic segmentation models"):
  with members:
    - "guided curriculum model adaptation and uncertainty aware evaluation for semantic nighttime image segmentation"
    - "adversarial examples are a natural consequence of test error in noise"
    - "gated scnn gated shape cnns for semantic segmentation"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "benchmarking robustness in object detection autonomous driving when winter is coming"
    - "deepcorrect correcting dnn models against image distortions"
    - "semantic foggy scene understanding with synthetic data"
    - "mobilenetv2 inverted residuals and linear bottlenecks"

Connected through hyperedge HE-6 (induced by source paper "drop an octave reducing spatial redundancy in convolutional neural networks with octave convolution"):
  with members:
    - "gluoncv and gluonnlp deep learning in computer vision and natural language processing"
    - "hetconv heterogeneous kernel based convolutions for deep cnns"
    - "deep high resolution representation learning for human pose estimation"
    - "elastic improving cnns with dynamic scaling policies"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "multi fiber networks for video recognition"
    - "regularized evolution for image classifier architecture search"
    - "a short note about kinetics 600"

Connected through hyperedge HE-7 (induced by source paper "stabilizing gans with octave convolutions"):
  with members:
    - "coco gan generation by parts via conditional coordinating"
    - "exploring randomly wired neural networks for image recognition"
    - "drop an octave reducing spatial redundancy in convolutional neural networks with octave convolution"
    - "spectral normalization for generative adversarial networks"
    - "darts differentiable architecture search"
    - "large scale gan training for high fidelity natural image synthesis"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "progressive growing of gans for improved quality stability and variation"

Connected through hyperedge HE-8 (induced by source paper "mgnet a unified framework of multigrid and convolutional neural network"):
  with members:
    - "pde net 2 0 learning pdes from data with a numeric symbolic hybrid deep network"
    - "learning neural pde solvers with convergence guarantees"
    - "exponential convergence of the deep neural network approximation for analytic functions"
    - "universality of deep convolutional neural networks"
    - "a flow model of neural networks"
    - "learning across scales a multiscale method for convolution neural networks"
    - "the reversible residual network backpropagation without storing activations"
    - "when image denoising meets high level vision tasks a deep learning approach"

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)