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: "structured receptive fields in cnns"
  Abstract: Learning powerful feature representations with CNNs is hard when training data are limited. Pre-training is one way to overcome this, but it requires large datasets sufficiently similar to the target domain. Another option is to design priors into the model, which can range from tuned hyperparameters to fully engineered representations like Scattering Networks. We combine these ideas into structured receptive field networks, a model which has a fixed filter basis and yet retains the flexibility of CNNs. This flexibility is achieved by expressing receptive fields in CNNs as a weighted sum over a fixed basis which is similar in spirit to Scattering Networks. The key difference is that we learn arbitrary effective filter sets from the basis rather than modeling the filters. This approach explicitly connects classical multiscale image analysis with general CNNs. With structured receptive field networks, we improve considerably over unstructured CNNs for small and medium dataset scenarios as well as over Scattering for large datasets. We validate our findings on ILSVRC2012, Cifar-10, Cifar-100 and MNIST. As a realistic small dataset example, we show state-of-the-art classification results on popular 3D MRI brain-disease datasets where pre-training is difficult due to a lack of large public datasets in a similar domain.

Connected through hyperedge HE-1 (induced by source paper "spatially adaptive filter units for compact and efficient deep neural networks"):
  with members:
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "spatially adaptive filter units for deep neural networks"
    - "active convolution learning the shape of convolution for image classification"
    - "deformable convolutional networks"
    - "dilated residual networks"
    - "understanding the effective receptive field in deep convolutional neural networks"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "rethinking atrous convolution for semantic image segmentation"

Connected through hyperedge HE-2 (induced by source paper "provably scale covariant continuous hierarchical networks based on scale normalized differential expressions coupled in cascade"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "blurring the line between structure and learning to optimize and adapt receptive fields"
    - "provably scale covariant networks from oriented quasi quadrature measures in cascade"
    - "drop an octave reducing spatial redundancy in convolutional neural networks with octave convolution"
    - "elastic improving cnns with dynamic scaling policies"
    - "from bow to cnn two decades of texture representation for texture classification"
    - "espnet efficient spatial pyramid of dilated convolutions for semantic segmentation"
    - "gabor convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "blurring the line between structure and learning to optimize and adapt receptive fields"):
  with members:
    - "making convolutional networks shift invariant again"
    - "darts differentiable architecture search"
    - "neural architecture search with bayesian optimisation and optimal transport"
    - "deep layer aggregation"
    - "deformable convolutional networks"
    - "dilated residual networks"
    - "understanding the effective receptive field in deep convolutional neural networks"
    - "understanding convolution for semantic segmentation"

Connected through hyperedge HE-4 (induced by source paper "associative convolutional layers"):
  with members:
    - "federated learning strategies for improving communication efficiency"
    - "a convergence theory for deep learning via over parameterization"
    - "deep gradient compression reducing the communication bandwidth for distributed training"
    - "adacomp adaptive residual gradient compression for data parallel distributed training"
    - "c ir cnn accelerating and compressing deep neural networks using block circulant weight matrices"
    - "flexpoint an adaptive numerical format for efficient training of deep neural networks"
    - "identity mappings in deep residual networks"
    - "densely connected convolutional networks"

Connected through hyperedge HE-5 (induced by source paper "translation insensitive cnns"):
  with members:
    - "spatially adaptive filter units for compact and efficient deep neural networks"
    - "blurring the line between structure and learning to optimize and adapt receptive fields"
    - "making convolutional networks shift invariant again"
    - "a systematic framework for natural perturbations from videos"
    - "rotdcf decomposition of convolutional filters for rotation equivariant deep networks"
    - "why do deep convolutional networks generalize so poorly to small image transformations"
    - "emergence of invariance and disentanglement in deep representations"
    - "inception v4 inception resnet and the impact of residual connections on learning"

Connected through hyperedge HE-6 (induced by source paper "harmonic networks with limited training samples"):
  with members:
    - "scattering networks for hybrid representation learning"
    - "gabor convolutional networks"
    - "wavelet based edge feature enhancement for convolutional neural networks"
    - "harmonic networks integrating spectral information into cnns"
    - "efficient convolutional network learning using parametric log based dual tree wavelet scatternet"
    - "a multi branch convolutional neural network for detecting double jpeg compression"
    - "aligned and non aligned double jpeg detection using convolutional neural networks"
    - "wavelet convolutional neural networks for texture classification"

Connected through hyperedge HE-7 (induced by source paper "learning to convolve a generalized weight tying approach"):
  with members:
    - "on the convergence of adam and beyond"
    - "roto translation covariant convolutional networks for medical image analysis"
    - "cubenet equivariance to 3d rotation and translation"
    - "3d steerable cnns learning rotationally equivariant features in volumetric data"
    - "learning steerable filters for rotation equivariant cnns"
    - "dynamic routing between capsules"
    - "learning so 3 equivariant representations with spherical cnns"
    - "the cityscapes dataset for semantic urban scene understanding"

Connected through hyperedge HE-8 (induced by source paper "sequential attend infer repeat generative modelling of moving objects"):
  with members:
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "world models"
    - "stochastic video generation with a learned prior"
    - "learning to decompose and disentangle representations for video prediction"
    - "recurrent ladder networks"
    - "end to end representation learning for correlation filter based tracking"
    - "unsupervised learning of disentangled representations from video"
    - "a simple neural network module for relational reasoning"

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)