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: "understanding deep architectures using a recursive convolutional network"
  Abstract: A key challenge in designing convolutional network models is sizing them appropriately. Many factors are involved in these decisions, including number of layers, feature maps, kernel sizes, etc. Complicating this further is the fact that each of these influence not only the numbers and dimensions of the activation units, but also the total number of parameters. In this paper we focus on assessing the independent contributions of three of these linked variables: The numbers of layers, feature maps, and parameters. To accomplish this, we employ a recursive convolutional network whose weights are tied between layers; this allows us to vary each of the three factors in a controlled setting. We find that while increasing the numbers of layers and parameters each have clear benefit, the number of feature maps (and hence dimensionality of the representation) appears ancillary, and finds most of its benefit through the introduction of more weights. Our results (i) empirically confirm the notion that adding layers alone increases computational power, within the context of convolutional layers, and (ii) suggest that precise sizing of convolutional feature map dimensions is itself of little concern; more attention should be paid to the number of parameters in these layers instead.

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 "cross x learning for fine grained visual categorization"):
  with members:
    - "maximum entropy fine grained classification"
    - "learning to navigate for fine grained classification"
    - "sampling matters in deep embedding learning"
    - "deep layer aggregation"
    - "dynamic computational time for visual attention"
    - "fully convolutional attention localization networks efficient attention localization for fine grained recognition"
    - "a unified multi scale deep convolutional neural network for fast object detection"
    - "ssd single shot multibox detector"

Connected through hyperedge HE-3 (induced by source paper "deraincyclegan an attention guided unsupervised benchmark for single image deraining and rainmaking"):
  with members:
    - "spatial attentive single image deraining with a high quality real rain dataset"
    - "progressive image deraining networks a better and simpler baseline"
    - "unsupervised domain specific deblurring via disentangled representations"
    - "unsupervised image super resolution using cycle in cycle generative adversarial networks"
    - "cycle dehaze enhanced cyclegan for single image dehazing"
    - "semi supervised transfer learning for image rain removal"
    - "density aware single image de raining using a multi stream dense network"
    - "drit diverse image to image translation via disentangled representations"

Connected through hyperedge HE-4 (induced by source paper "urban sound tagging using convolutional neural networks"):
  with members:
    - "learning sound event classifiers from web audio with noisy labels"
    - "end to end environmental sound classification using a 1d convolutional neural network"
    - "attention based convolutional recurrent neural network for environmental sound classification"
    - "on the convergence of adam and beyond"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "random erasing data augmentation"
    - "audio spectrogram representations for processing with convolutional neural networks"

Connected through hyperedge HE-5 (induced by source paper "multi scale deep neural networks for real image super resolution"):
  with members:
    - "wide activation for efficient and accurate image super resolution"
    - "enhanced deep residual networks for single image super resolution"
    - "accelerating the super resolution convolutional neural network"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "densely connected convolutional networks"
    - "real time single image and video super resolution using an efficient sub pixel convolutional neural network"
    - "deep multi scale convolutional neural network for dynamic scene deblurring"
    - "real time video super resolution with spatio temporal networks and motion compensation"

Connected through hyperedge HE-6 (induced by source paper "improving learnability of neural networks adding supplementary axes to disentangle data representation"):
  with members:
    - "generative adversarial network in medical imaging a review"
    - "adversarial attacks against medical deep learning systems"
    - "adversarial vulnerability for any classifier"
    - "understanding deep learning requires rethinking generalization"
    - "conditional generative adversarial nets"
    - "on the number of response regions of deep feed forward networks with piece wise linear activations"

Connected through hyperedge HE-7 (induced by source paper "emergent structures and lifetime structure evolution in artificial neural networks"):
  with members:
    - "continual learning via neural pruning"
    - "discriminative recurrent sparse auto encoders"

Connected through hyperedge HE-8 (induced by source paper "residual guide feature fusion network for single image deraining"):
  with members:
    - "density aware single image de raining using a multi stream dense network"
    - "densely connected pyramid dehazing network"
    - "clearing the skies a deep network architecture for single image rain removal"
    - "multi scale dense networks for resource efficient image classification"
    - "image de raining using a conditional generative adversarial network"
    - "memnet a persistent memory network for image restoration"
    - "an all in one network for dehazing and beyond"
    - "dehazenet an end to end system for single image haze removal"

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)