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: "mixed link networks"
  Abstract: Basing on the analysis by revealing the equivalence of modern networks, we find that both ResNet and DenseNet are essentially derived from the same "dense topology", yet they only differ in the form of connection -- addition (dubbed "inner link") vs. concatenation (dubbed "outer link"). However, both two forms of connections have the superiority and insufficiency. To combine their advantages and avoid certain limitations on representation learning, we present a highly efficient and modularized Mixed Link Network (MixNet) which is equipped with flexible inner link and outer link modules. Consequently, ResNet, DenseNet and Dual Path Network (DPN) can be regarded as a special case of MixNet, respectively. Furthermore, we demonstrate that MixNets can achieve superior efficiency in parameter over the state-of-the-art architectures on many competitive datasets like CIFAR-10/100, SVHN and ImageNet.

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"
    - "residual networks of residual networks multilevel residual networks"

Connected through hyperedge HE-2 (induced by source paper "efficient and accurate arbitrary shaped text detection with pixel aggregation network"):
  with members:
    - "rethinking rgb d salient object detection models datasets and large scale benchmarks"
    - "selective kernel networks"
    - "shape robust text detection with progressive scale expansion network"
    - "rotation sensitive regression for oriented scene text detection"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "arbitrary oriented scene text detection via rotation proposals"
    - "cu net coupled u nets"
    - "textsnake a flexible representation for detecting text of arbitrary shapes"

Connected through hyperedge HE-3 (induced by source paper "spatial group wise enhance improving semantic feature learning in convolutional networks"):
  with members:
    - "res2net a new multi scale backbone architecture"
    - "gcnet non local networks meet squeeze excitation networks and beyond"
    - "searching for mobilenetv3"
    - "selective kernel networks"
    - "weight standardization"
    - "bam bottleneck attention module"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "image super resolution using very deep residual channel attention networks"

Connected through hyperedge HE-4 (induced by source paper "gated convolutional networks with hybrid connectivity for image classification"):
  with members:
    - "res2net a new multi scale backbone architecture"
    - "gcnet non local networks meet squeeze excitation networks and beyond"
    - "selective kernel networks"
    - "log densenet how to sparsify a densenet"
    - "dropblock a regularization method for convolutional networks"
    - "regularized evolution for image classifier architecture search"
    - "autoaugment learning augmentation policies from data"
    - "sparsely aggregated convolutional networks"

Connected through hyperedge HE-5 (induced by source paper "dianet dense and implicit attention network"):
  with members:
    - "towards universal object detection by domain attention"
    - "gcnet non local networks meet squeeze excitation networks and beyond"
    - "selective kernel networks"
    - "bam bottleneck attention module"
    - "gather excite exploiting feature context in convolutional neural networks"
    - "cbam convolutional block attention module"
    - "non local neural networks"
    - "attention is all you need"

Connected through hyperedge HE-6 (induced by source paper "understanding the disharmony between weight normalization family and weight decay e shifted l 2 regularizer"):
  with members:
    - "on the variance of the adaptive learning rate and beyond"
    - "spatial group wise enhance improving semantic feature learning in convolutional networks"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "weight standardization"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "differentiable learning to normalize via switchable normalization"
    - "three mechanisms of weight decay regularization"
    - "highly scalable deep learning training system with mixed precision training imagenet in four minutes"

Connected through hyperedge HE-7 (induced by source paper "sparsely aggregated convolutional networks"):
  with members:
    - "log densenet how to sparsify a densenet"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "the reversible residual network backpropagation without storing activations"
    - "deep layer aggregation"
    - "deep expander networks efficient deep networks from graph theory"
    - "dual path networks"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "condensenet an efficient densenet using learned group convolutions"

Connected through hyperedge HE-8 (induced by source paper "when work matters transforming classical network structures to graph cnn"):
  with members:
    - "adaptive graph convolutional neural networks"
    - "shift aggregate extract networks"
    - "dgcnn disordered graph convolutional neural network based on the gaussian mixture model"
    - "geometric deep learning on graphs and manifolds using mixture model cnns"
    - "densely connected convolutional networks"
    - "time variant graph classification"
    - "learning convolutional neural networks for graphs"
    - "deep convolutional networks on graph structured data"

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.LG(Machine Learning)