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: "deep fried convnets"
  Abstract: The fully connected layers of a deep convolutional neural network typically contain over 90% of the network parameters, and consume the majority of the memory required to store the network parameters. Reducing the number of parameters while preserving essentially the same predictive performance is critically important for operating deep neural networks in memory constrained environments such as GPUs or embedded devices. #R##N#In this paper we show how kernel methods, in particular a single Fastfood layer, can be used to replace all fully connected layers in a deep convolutional neural network. This novel Fastfood layer is also end-to-end trainable in conjunction with convolutional layers, allowing us to combine them into a new architecture, named deep fried convolutional networks, which substantially reduces the memory footprint of convolutional networks trained on MNIST and ImageNet with no drop in predictive performance.

Connected through hyperedge HE-1 (induced by source paper "low memory neural network training a technical report"):
  with members:
    - "traditional and heavy tailed self regularization in neural network models"
    - "pay less attention with lightweight and dynamic convolutions"
    - "learning fast algorithms for linear transforms using butterfly factorizations"
    - "trained quantization thresholds for accurate and efficient fixed point inference of deep neural networks"
    - "memory efficient adaptive optimization"
    - "sparse evolutionary deep learning with over one million artificial neurons on commodity hardware"
    - "fixup initialization residual learning without normalization"
    - "full deep neural network training on a pruned weight budget"

Connected through hyperedge HE-2 (induced by source paper "parameter efficient training of deep convolutional neural networks by dynamic sparse reparameterization"):
  with members:
    - "measuring the intrinsic dimension of objective landscapes"
    - "scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science"
    - "recovering from random pruning on the plasticity of deep convolutional neural networks"
    - "training wide residual networks for deployment using a single bit for each weight"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "a systematic dnn weight pruning framework using alternating direction method of multipliers"
    - "network compression using correlation analysis of layer responses"
    - "data driven sparse structure selection for deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "knowledge squeezed adversarial network compression"):
  with members:
    - "ktan knowledge transfer adversarial network"
    - "model compression via distillation and quantization"
    - "attention guided answer distillation for machine reading comprehension"
    - "learn to pay attention"
    - "adversarial network compression"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "darkrank accelerating deep metric learning via cross sample similarities transfer"

Connected through hyperedge HE-4 (induced by source paper "quantifying long range dependence in language and user behavior to improve rnns"):
  with members:
    - "towards neural mixture recommender for long range dependent user sequences"
    - "learning longer term dependencies in rnns with auxiliary losses"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "contextual sequence modeling for recommendation with recurrent neural networks"
    - "attention is all you need"
    - "dilated recurrent neural networks"
    - "regularizing and optimizing lstm language models"
    - "on orthogonality and learning recurrent networks with long term dependencies"

Connected through hyperedge HE-5 (induced by source paper "compression of recurrent neural networks for efficient language modeling"):
  with members:
    - "trellis networks for sequence modeling"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "bayesian compression for natural language processing"
    - "online embedding compression for text classification using low rank matrix factorization"
    - "breaking the softmax bottleneck a high rank rnn language model"
    - "attention is all you need"
    - "regularizing and optimizing lstm language models"
    - "compressing recurrent neural network with tensor train"

Connected through hyperedge HE-6 (induced by source paper "doctor of crosswise reducing over parametrization in neural networks"):
  with members:
    - "efficient interactive annotation of segmentation datasets with polygon rnn"
    - "revisiting random binning features fast convergence and strong parallelizability"
    - "autoaugment learning augmentation policies from data"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "training deep networks with synthetic data bridging the reality gap by domain randomization"
    - "mckernel approximate kernel expansions in log linear time through randomization"
    - "progressive growing of gans for improved quality stability and variation"
    - "high resolution deep convolutional generative adversarial networks"

Connected through hyperedge HE-7 (induced by source paper "learning fast algorithms for linear transforms using butterfly factorizations"):
  with members:
    - "neural arithmetic logic units"
    - "quadrature based features for kernel approximation"
    - "learning compressed transforms with low displacement rank"
    - "c ir cnn accelerating and compressing deep neural networks using block circulant weight matrices"
    - "a semantic loss function for deep learning with symbolic knowledge"
    - "strassennets deep learning with a multiplication budget"
    - "hyperband a novel bandit based approach to hyperparameter optimization"
    - "neural combinatorial optimization with reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "few shot learning with localization in realistic settings"):
  with members:
    - "deep covariance descriptors for facial expression recognition"
    - "covariance pooling for facial expression recognition"
    - "low shot learning from imaginary data"
    - "power normalizing second order similarity network for few shot learning"
    - "prototypical networks for few shot learning"
    - "extreme clicking for efficient object annotation"
    - "a simple neural attentive meta learner"
    - "residual attention network for image classification"

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)