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: "doubly convolutional neural networks"
  Abstract: Building large models with parameter sharing accounts for most of the success of deep convolutional neural networks (CNNs). In this paper, we propose doubly convolutional neural networks (DCNNs), which significantly improve the performance of CNNs by further exploring this idea. In stead of allocating a set of convolutional filters that are independently learned, a DCNN maintains groups of filters where filters within each group are translated versions of each other. Practically, a DCNN can be easily implemented by a two-step convolution procedure, which is supported by most modern deep learning libraries. We perform extensive experiments on three image classification benchmarks: CIFAR-10, CIFAR-100 and ImageNet, and show that DCNNs consistently outperform other competing architectures. We have also verified that replacing a convolutional layer with a doubly convolutional layer at any depth of a CNN can improve its performance. Moreover, various design choices of DCNNs are demonstrated, which shows that DCNN can serve the dual purpose of building more accurate models and/or reducing the memory footprint without sacrificing the accuracy.

Connected through hyperedge HE-1 (induced by source paper "bert of theseus compressing bert by progressive module replacing"):
  with members:
    - "are sixteen heads really better than one"
    - "reducing transformer depth on demand with structured dropout"
    - "distilbert a distilled version of bert smaller faster cheaper and lighter"
    - "q bert hessian based ultra low precision quantization of bert"
    - "well read students learn better on the importance of pre training compact models"
    - "deep double descent where bigger models and more data hurt"
    - "multilingual neural machine translation with knowledge distillation"
    - "mass masked sequence to sequence pre training for language generation"

Connected through hyperedge HE-2 (induced by source paper "a privacy preserving dnn pruning and mobile acceleration framework"):
  with members:
    - "asymptotic soft filter pruning for deep convolutional neural networks"
    - "pconv the missing but desirable sparsity in dnn weight pruning for real time execution on mobile devices"
    - "data free learning of student networks"
    - "2pfpce two phase filter pruning based on conditional entropy"
    - "amc automl for model compression and acceleration on mobile devices"
    - "private model compression via knowledge distillation"
    - "discrimination aware channel pruning for deep neural networks"
    - "a systematic dnn weight pruning framework using alternating direction method of multipliers"

Connected through hyperedge HE-3 (induced by source paper "collaborative inference for efficient remote monitoring"):
  with members:
    - "deep learning for time series classification a review"
    - "efficient inference on deep neural networks by dynamic representations and decision gates"
    - "deep feature compression for collaborative object detection"
    - "doubly nested network for resource efficient inference"
    - "cardiologist level arrhythmia detection with convolutional neural networks"
    - "deep learning with convolutional neural networks for eeg decoding and visualization"
    - "xgboost a scalable tree boosting system"
    - "group equivariant convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "multi level wavelet convolutional neural networks"):
  with members:
    - "parallel grid pooling for data augmentation"
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "image super resolution using very deep residual channel attention networks"
    - "toward convolutional blind denoising of real photographs"
    - "framing u net via deep convolutional framelets application to sparse view ct"
    - "deep laplacian pyramid networks for fast and accurate super resolution"
    - "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration"
    - "enhanced deep residual networks for single image super resolution"

Connected through hyperedge HE-5 (induced by source paper "patient knowledge distillation for bert model compression"):
  with members:
    - "multilingual neural machine translation with knowledge distillation"
    - "improving multi task deep neural networks via knowledge distillation for natural language understanding"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "roberta a robustly optimized bert pretraining approach"
    - "sdnet contextualized attention based deep network for conversational question answering"
    - "model compression via distillation and quantization"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "deep contextualized word representations"

Connected through hyperedge HE-6 (induced by source paper "a hybrid approach with optimization and metric based meta learner for few shot learning"):
  with members:
    - "multi level semantic feature augmentation for one shot learning"
    - "few shot learning with meta metric learners"
    - "adapted deep embeddings a synthesis of methods for k shot inductive transfer learning"
    - "meta learning with latent embedding optimization"
    - "bayesian model agnostic meta learning"
    - "deep meta learning learning to learn in the concept space"
    - "tadam task dependent adaptive metric for improved few shot learning"
    - "diverse few shot text classification with multiple metrics"

Connected through hyperedge HE-7 (induced by source paper "coreset based neural network compression"):
  with members:
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "incremental learning through deep adaptation"
    - "deformable convolutional networks"
    - "thinet a filter level pruning method for deep neural network compression"
    - "compression aware training of deep networks"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "n2n learning network to network compression via policy gradient reinforcement learning"
    - "mask r cnn"

Connected through hyperedge HE-8 (induced by source paper "multi layer pruning framework for compressing single shot multibox detector"):
  with members:
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "thinet a filter level pruning method for deep neural network compression"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "towards lightweight convolutional neural networks for object detection"
    - "channel pruning for accelerating very deep neural networks"
    - "paying more attention to attention improving the performance of convolutional neural networks via attention transfer"
    - "ssd single shot multibox detector"
    - "dsd regularizing deep neural networks with dense sparse dense training flow"

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)