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 are exponential ensembles of relatively shallow networks"
  Abstract: In this work, we introduce a novel interpretation of residual networks showing they are exponential ensembles. This observation is supported by a large-scale lesion study that demonstrates they behave just like ensembles at test time. Subsequently, we perform an analysis showing these ensembles mostly consist of networks that are each relatively shallow. For example, contrary to our expectations, most of the gradient in a residual network with 110 layers comes from an ensemble of very short networks, i.e., only 10-34 layers deep. This suggests that in addition to describing neural networks in terms of width and depth, there is a third dimension: multiplicity, the size of the implicit ensemble. Ultimately, residual networks do not resolve the vanishing gradient problem by preserving gradient flow throughout the entire depth of the network - rather, they avoid the problem simply by ensembling many short networks together. This insight reveals that depth is still an open research question and invites the exploration of the related notion of multiplicity.

Connected through hyperedge HE-1 (induced by source paper "the effect of data ordering in image classification"):
  with members:
    - "bayesnas a bayesian approach for neural architecture search"
    - "progressive neural architecture search"
    - "learning transferable architectures for scalable image recognition"
    - "deep networks with stochastic depth"
    - "identity mappings in deep residual networks"
    - "densely connected convolutional networks"
    - "neural architecture search with reinforcement learning"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-2 (induced by source paper "mdfn multi scale deep feature learning network for object detection"):
  with members:
    - "unsupervised deep feature transfer for low resolution image classification"
    - "adversarially approximated autoencoder for image generation and manipulation"
    - "boosting occluded image classification via subspace decomposition based estimation of deep features"
    - "mdcn multi scale deep inception convolutional neural networks for efficient object detection"
    - "yolov3 an incremental improvement"
    - "a 2 nets double attention networks"
    - "learning depth from single images with deep neural network embedding focal length"
    - "non local neural networks"

Connected through hyperedge HE-3 (induced by source paper "bilinear attention networks"):
  with members:
    - "learning to count objects in natural images for visual question answering"
    - "interpretable and globally optimal prediction for textual grounding using image concepts"
    - "mutan multimodal tucker fusion for visual question answering"
    - "tips and tricks for visual question answering learnings from the 2017 challenge"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "discriminative learning of open vocabulary object retrieval and localization by negative phrase augmentation"
    - "interpretable counting for visual question answering"
    - "dual attention networks for multimodal reasoning and matching"

Connected through hyperedge HE-4 (induced by source paper "recurrent knowledge distillation"):
  with members:
    - "sharesnet reducing residual network parameter number by sharing weights"
    - "mask r cnn"
    - "resfeats residual network based features for image classification"
    - "paying more attention to attention improving the performance of convolutional neural networks via attention transfer"
    - "sketching and neural networks"
    - "highway and residual networks learn unrolled iterative estimation"
    - "feature pyramid networks for object detection"
    - "wide residual networks"

Connected through hyperedge HE-5 (induced by source paper "overcoming the vanishing gradient problem in plain recurrent networks"):
  with members:
    - "gated orthogonal recurrent units on learning to forget"
    - "learning simpler language models with the differential state framework"
    - "all you need is beyond a good init exploring better solution for training extremely deep convolutional neural networks with orthonormality and modulation"
    - "phased lstm accelerating recurrent network training for long or event based sequences"
    - "identity mappings in deep residual networks"
    - "highway and residual networks learn unrolled iterative estimation"
    - "tunable efficient unitary neural networks eunn and their application to rnns"
    - "all you need is a good init"

Connected through hyperedge HE-6 (induced by source paper "mdnet a semantically and visually interpretable medical image diagnosis network"):
  with members:
    - "image captioning with semantic attention"
    - "disturblabel regularizing cnn on the loss layer"
    - "deep networks with stochastic depth"
    - "attention correctness in neural image captioning"
    - "deep convolutional neural networks for computer aided detection cnn architectures dataset characteristics and transfer learning"
    - "identity mappings in deep residual networks"
    - "learning to read chest x rays recurrent neural cascade model for automated image annotation"
    - "densely connected convolutional networks"

Connected through hyperedge HE-7 (induced by source paper "all you need is beyond a good init exploring better solution for training extremely deep convolutional neural networks with orthonormality and modulation"):
  with members:
    - "training region based object detectors with online hard example mining"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "residual networks behave like ensembles of relatively shallow networks"
    - "learning to refine object segments"
    - "wide residual networks"
    - "fractalnet ultra deep neural networks without residuals"
    - "weight normalization a simple reparameterization to accelerate training of deep neural networks"
    - "optimization on submanifolds of convolution kernels in cnns"

Connected through hyperedge HE-8 (induced by source paper "network sketching exploiting binary structure in deep cnns"):
  with members:
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "xnor net imagenet classification using binary convolutional neural networks"
    - "local binary convolutional neural networks"
    - "dynamic network surgery for efficient dnns"
    - "binaryconnect training deep neural networks with binary weights during propagations"
    - "fixed point quantization of deep convolutional networks"
    - "convolutional neural networks with low rank regularization"
    - "learning both weights and connections for efficient 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)