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: "residual networks of residual networks multilevel residual networks"
  Abstract: A residual networks family with hundreds or even thousands of layers dominates major image recognition tasks, but building a network by simply stacking residual blocks inevitably limits its optimization ability. This paper proposes a novel residual network architecture, residual networks of residual networks (RoR), to dig the optimization ability of residual networks. RoR substitutes optimizing residual mapping of residual mapping for optimizing original residual mapping. In particular, RoR adds levelwise shortcut connections upon original residual networks to promote the learning capability of residual networks. More importantly, RoR can be applied to various kinds of residual networks (ResNets, Pre-ResNets, and WRN) and significantly boost their performance. Our experiments demonstrate the effectiveness and versatility of RoR, where it achieves the best performance in all residual-network-like structures. Our RoR-3-WRN58-4 + SD models achieve new state-of-the-art results on CIFAR-10, CIFAR-100, and SVHN, with the test errors of 3.77%, 19.73%, and 1.59%, respectively. RoR-3 models also achieve state-of-the-art results compared with ResNets on the ImageNet data set.

Connected through hyperedge HE-1 (induced by source paper "π nets deep polynomial neural networks"):
  with members:
    - "implicit generation and generalization in energy based models"
    - "polygan high order polynomial generators"
    - "on the convergence of adam and beyond"
    - "semantic image synthesis with spatially adaptive normalization"
    - "on the expressive power of deep polynomial neural networks"
    - "adversarial training of partially invertible variational autoencoders"
    - "generating 3d faces using convolutional mesh autoencoders"
    - "mixed link networks"

Connected through hyperedge HE-2 (induced by source paper "selective kernel networks"):
  with members:
    - "igcv3 interleaved low rank group convolutions for efficient deep neural networks"
    - "bam bottleneck attention module"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "image super resolution using very deep residual channel attention networks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "person search via a mask guided two stream cnn model"
    - "igcv 2 interleaved structured sparse convolutional neural networks"
    - "cbam convolutional block attention module"

Connected through hyperedge HE-3 (induced by source paper "rethinking curriculum learning with incremental labels and adaptive compensation"):
  with members:
    - "on the power of curriculum learning in training deep networks"
    - "curriculum learning by transfer learning theory and experiments with deep networks"
    - "experience replay for continual learning"
    - "learning to teach"
    - "shakedrop regularization for deep residual learning"
    - "drop activation implicit parameter reduction and harmonic regularization"
    - "label refinery improving imagenet classification through label progression"
    - "end to end incremental learning"

Connected through hyperedge HE-4 (induced by source paper "competing ratio loss for discriminative multi class image classification"):
  with members:
    - "object part attention model for fine grained image classification"
    - "taming the cross entropy loss"
    - "convolutional neural networks with alternately updated clique"
    - "negative log likelihood ratio loss for deep neural network classification"
    - "sphereface deep hypersphere embedding for face recognition"
    - "focal loss for dense object detection"
    - "age group and gender estimation in the wild with deep ror architecture"
    - "dual path networks"

Connected through hyperedge HE-5 (induced by source paper "4 connected shift residual networks"):
  with members:
    - "synetgy algorithm hardware co design for convnet accelerators on embedded fpgas"
    - "all you need is a few shifts designing efficient convolutional neural networks for image classification"
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "benchmark analysis of representative deep neural network architectures"
    - "motion feature network fixed motion filter for action recognition"
    - "constructing fast network through deconstruction of convolution"
    - "learning transferable architectures for scalable image recognition"

Connected through hyperedge HE-6 (induced by source paper "mixture separability loss in a deep convolutional network for image classification"):
  with members:
    - "pyramidal ror for image classification"
    - "on loss functions for deep neural networks in classification"
    - "aggregated residual transformations for deep neural networks"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "identity mappings in deep residual networks"
    - "wide residual networks"
    - "xception deep learning with depthwise separable convolutions"
    - "deep pyramidal residual networks"

Connected through hyperedge HE-7 (induced by source paper "esrgan enhanced super resolution generative adversarial networks"):
  with members:
    - "recovering realistic texture in image super resolution by deep spatial feature transform"
    - "the relativistic discriminator a key element missing from standard gan"
    - "the 2018 pirm challenge on perceptual image super resolution"
    - "image super resolution using very deep residual channel attention networks"
    - "unsupervised image super resolution using cycle in cycle generative adversarial networks"
    - "the perception distortion tradeoff"
    - "spectral normalization for generative adversarial networks"
    - "crafting a toolchain for image restoration by deep reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "fine grained age estimation in the wild with attention lstm networks"):
  with members:
    - "deep label distribution learning with label ambiguity"
    - "age group and gender estimation in the wild with deep ror architecture"
    - "video summarization with attention based encoder decoder networks"
    - "dager deep age gender and emotion recognition using convolutional neural network"
    - "pyramidal ror for image classification"
    - "apparent age estimation using ensemble of deep learning models"
    - "deep networks with stochastic depth"
    - "squared earth mover s distance based loss for training deep neural networks"

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)