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: "bridging the gaps between residual learning recurrent neural networks and visual cortex"
  Abstract: We discuss relations between Residual Networks (ResNet), Recurrent Neural Networks (RNNs) and the primate visual cortex. We begin with the observation that a shallow RNN is exactly equivalent to a very deep ResNet with weight sharing among the layers. A direct implementation of such a RNN, although having orders of magnitude fewer parameters, leads to a performance similar to the corresponding ResNet. We propose 1) a generalization of both RNN and ResNet architectures and 2) the conjecture that a class of moderately deep RNNs is a biologically-plausible model of the ventral stream in visual cortex. We demonstrate the eectiveness of the architectures by testing them on the CIFAR-10 dataset.

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"
    - "resnet in resnet generalizing residual architectures"
    - "deep networks with stochastic depth"

Connected through hyperedge HE-2 (induced by source paper "fundamentals of recurrent neural network rnn and long short term memory lstm network"):
  with members:
    - "latent odes for irregularly sampled time series"
    - "long short term memory as a dynamically computed element wise weighted sum"
    - "stable architectures for deep neural networks"
    - "deep neural networks motivated by partial differential equations"
    - "neural ordinary differential equations"
    - "recent advances in recurrent neural networks"
    - "nais net stable deep networks from non autonomous differential equations"
    - "lstm a search space odyssey"

Connected through hyperedge HE-3 (induced by source paper "learning implicitly recurrent cnns through parameter sharing"):
  with members:
    - "convolutional neural networks with alternately updated clique"
    - "regularized evolution for image classifier architecture search"
    - "neural arithmetic logic units"
    - "snas stochastic neural architecture search"
    - "sparsely aggregated convolutional networks"
    - "efficient neural architecture search via parameter sharing"
    - "progressive neural architecture search"
    - "making neural programming architectures generalize via recursion"

Connected through hyperedge HE-4 (induced by source paper "understanding and improving transformer from a multi particle dynamic system point of view"):
  with members:
    - "pay less attention with lightweight and dynamic convolutions"
    - "transformer xl attentive language models beyond a fixed length context"
    - "fixup initialization residual learning without normalization"
    - "universal transformers"
    - "weighted transformer network for machine translation"
    - "self attention with relative position representations"
    - "dynamically unfolding recurrent restorer a moving endpoint control method for image restoration"
    - "glue a multi task benchmark and analysis platform for natural language understanding"

Connected through hyperedge HE-5 (induced by source paper "adaptive and iteratively improving recurrent lateral connections"):
  with members:
    - "video action recognition via neural architecture searching"
    - "graph hypernetworks for neural architecture search"
    - "self supervised spatiotemporal feature learning via video rotation prediction"
    - "spatio temporal channel correlation networks for action classification"
    - "iamnn iterative and adaptive mobile neural network for efficient image classification"
    - "cooperative learning of audio and video models from self supervised synchronization"
    - "densepose dense human pose estimation in the wild"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"

Connected through hyperedge HE-6 (induced by source paper "feedback network for image super resolution"):
  with members:
    - "fast and accurate single image super resolution via information distillation network"
    - "image super resolution using very deep residual channel attention networks"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "top down feedback for crowd counting convolutional neural network"
    - "a fully progressive approach to single image super resolution"
    - "residual dense network for image super resolution"
    - "deep back projection networks for super resolution"
    - "image super resolution via dual state recurrent networks"

Connected through hyperedge HE-7 (induced by source paper "disentangling neural mechanisms for perceptual grouping"):
  with members:
    - "deep predictive coding network for object recognition"
    - "one shot segmentation in clutter"
    - "can recurrent neural networks warp time"
    - "sample efficient image segmentation through recurrence"
    - "robust neural circuit reconstruction from serial electron microscopy with convolutional recurrent networks"
    - "superhuman accuracy on the snemi3d connectomics challenge"
    - "dynamic routing between capsules"
    - "understanding deep learning requires rethinking generalization"

Connected through hyperedge HE-8 (induced by source paper "brain like object recognition with high performing shallow recurrent anns"):
  with members:
    - "do better imagenet models transfer better"
    - "exploring the limits of weakly supervised pretraining"
    - "iamnn iterative and adaptive mobile neural network for efficient image classification"
    - "progressive neural architecture search"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "residual connections encourage iterative inference"
    - "aggregated residual transformations for 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.LG(Machine Learning)