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: "resnet in resnet generalizing residual architectures"
  Abstract: Residual networks (ResNets) have recently achieved state-of-the-art on challenging computer vision tasks. We introduce Resnet in Resnet (RiR): a deep dual-stream architecture that generalizes ResNets and standard CNNs and is easily implemented with no computational overhead. RiR consistently improves performance over ResNets, outperforms architectures with similar amounts of augmentation on CIFAR-10, and establishes a new state-of-the-art on CIFAR-100.

Connected through hyperedge HE-1 (induced by source paper "convolutional networks with dense connectivity"):
  with members:
    - "log densenet how to sparsify a densenet"
    - "h denseunet hybrid densely connected unet for liver and liver tumor segmentation from ct volumes"
    - "squeeze and excitation networks"
    - "dual path networks"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "condensenet an efficient densenet using learned group convolutions"
    - "deep networks with stochastic depth"
    - "identity mappings in deep residual networks"

Connected through hyperedge HE-2 (induced by source paper "research on a new convolutional neural network model combined with random edges adding"):
  with members:
    - "aggregated residual transformations for deep neural networks"
    - "inception v4 inception resnet and the impact of residual connections on learning"

Connected through hyperedge HE-3 (induced by source paper "choicenet cnn learning through choice of multiple feature map representations"):
  with members:
    - "adaptive affinity fields for semantic segmentation"
    - "efficient dense modules of asymmetric convolution for real time semantic segmentation"
    - "recurrent scene parsing with perspective understanding in the loop"
    - "understanding convolution for semantic segmentation"
    - "residual conv deconv grid network for semantic segmentation"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "deep networks with stochastic depth"
    - "refinenet multi path refinement networks for high resolution semantic segmentation"

Connected through hyperedge HE-4 (induced by source paper "swnet small world neural networks and rapid convergence"):
  with members:
    - "random graph models for dynamic networks"
    - "aggregated residual transformations for deep neural networks"
    - "deep networks with stochastic depth"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "identity mappings in deep residual networks"
    - "densely connected convolutional networks"
    - "cyclical learning rates for training neural networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-5 (induced by source paper "robust learning with implicit residual networks"):
  with members:
    - "imexnet a forward stable deep neural network"
    - "lipschitz margin training scalable certification of perturbation invariance for deep neural networks"
    - "spectral normalization for generative adversarial networks"
    - "stable architectures for deep neural networks"
    - "deep neural networks motivated by partial differential equations"
    - "loss sensitive generative adversarial networks on lipschitz densities"
    - "the reversible residual network backpropagation without storing activations"
    - "reversible architectures for arbitrarily deep residual neural networks"

Connected through hyperedge HE-6 (induced by source paper "automated let s play commentary"):
  with members:
    - "automated rationale generation a technique for explainable ai and its effects on human perceptions"
    - "universal sentence encoder"
    - "towards automated let s play commentary"
    - "procedural content generation via machine learning pcgml"
    - "how the experts do it assessing and explaining agent behaviors in real time strategy games"
    - "empirical evaluation of rectified activations in convolutional network"
    - "taking a deeper look at pedestrians"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "recent advances in deep learning an overview"):
  with members:
    - "deep learning a critical appraisal"
    - "adversarial attacks on neural network policies"
    - "deep learning for environmentally robust speech recognition an overview of recent developments"
    - "lstm a search space odyssey"
    - "a knowledge grounded neural conversation model"
    - "me r cnn multi expert region based cnn for object detection"
    - "on the origin of deep learning"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-8 (induced by source paper "sparsely aggregated convolutional networks"):
  with members:
    - "log densenet how to sparsify a densenet"
    - "mixed link networks"
    - "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"

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)