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 learning using linear support vector machines"
  Abstract: Recently, fully-connected and convolutional neural networks have been trained to achieve state-of-the-art performance on a wide variety of tasks such as speech recognition, image classification, natural language processing, and bioinformatics. For classification tasks, most of these "deep learning" models employ the softmax activation function for prediction and minimize cross-entropy loss. In this paper, we demonstrate a small but consistent advantage of replacing the softmax layer with a linear support vector machine. Learning minimizes a margin-based loss instead of the cross-entropy loss. While there have been various combinations of neural nets and SVMs in prior art, our results using L2-SVMs show that by simply replacing softmax with linear SVMs gives significant gains on popular deep learning datasets MNIST, CIFAR-10, and the ICML 2013 Representation Learning Workshop's face expression recognition challenge.

Connected through hyperedge HE-1 (induced by source paper "mixed precision deep learning based on computational memory"):
  with members:
    - "long short term memory networks in memristor crossbar arrays"
    - "neuromorphic computing with multi memristive synapses"
    - "mixed precision in memory computing"
    - "training lstm networks with resistive cross point devices"
    - "bridging the accuracy gap for 2 bit quantized neural networks qnn"
    - "training and inference with integers in deep neural networks"
    - "an analysis of neural language modeling at multiple scales"
    - "an improved evaluation framework for generative adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "fast dense residual network enhancing global dense feature flow for text recognition"):
  with members:
    - "fast densenet towards efficient and accurate text recognition with fast dense networks"
    - "fully convolutional intensive feature flow neural network for text recognition"
    - "denoising prior driven deep neural network for image restoration"
    - "esir end to end scene text recognition via iterative image rectification"
    - "residual dense network for image super resolution"
    - "aon towards arbitrarily oriented text recognition"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "reading scene text with attention convolutional sequence modeling"

Connected through hyperedge HE-3 (induced by source paper "suppressing uncertainties for large scale facial expression recognition"):
  with members:
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "noise tolerant paradigm for training face recognition cnns"
    - "region attention networks for pose and occlusion robust facial expression recognition"
    - "curriculumnet weakly supervised learning from large scale web images"
    - "arcface additive angular margin loss for deep face recognition"
    - "emotion recognition in speech using cross modal transfer in the wild"
    - "sphereface deep hypersphere embedding for face recognition"
    - "mentornet learning data driven curriculum for very deep neural networks on corrupted labels"

Connected through hyperedge HE-4 (induced by source paper "analysis of diffractive optical neural networks and their integration with electronic neural networks"):
  with members:
    - "on chip optical convolutional neural networks"
    - "all optical machine learning using diffractive deep neural networks"
    - "pcnna a photonic convolutional neural network accelerator"
    - "reinforcement learning in a large scale photonic recurrent neural network"
    - "toward fast neural computing using all photonic phase change spiking neurons"
    - "principles of neuromorphic photonics"
    - "be careful what you backpropagate a case for linear output activations gradient boosting"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"

Connected through hyperedge HE-5 (induced by source paper "designing gans a likelihood ratio approach"):
  with members:
    - "lipschitz generative adversarial nets"
    - "generative adversarial networks an overview"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "the numerics of gans"
    - "improved training of wasserstein gans"
    - "on loss functions for deep neural networks in classification"
    - "energy based generative adversarial network"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "support vector machines on the d wave quantum annealer"):
  with members:
    - "nonnegative binary matrix factorization with a d wave quantum annealer"
    - "a study of complex deep learning networks on high performance neuromorphic and quantum computers"
    - "robustness and regularization of support vector machines"

Connected through hyperedge HE-7 (induced by source paper "continuous emotion recognition via deep convolutional autoencoder and support vector regressor"):
  with members:
    - "end to end multimodal emotion recognition using deep neural networks"
    - "going deeper in facial expression recognition using deep neural networks"
    - "emotional expression classification using time series kernels"

Connected through hyperedge HE-8 (induced by source paper "a survey of techniques all classifiers can learn from deep networks models optimizations and regularization"):
  with members:
    - "deep learning for sensor based activity recognition a survey"
    - "multi layered gradient boosting decision trees"
    - "gan augmentation augmenting training data using generative adversarial networks"
    - "deep ehr a survey of recent advances in deep learning techniques for electronic health record ehr analysis"
    - "sampling strategies in siamese networks for unsupervised speech representation learning"
    - "deep neural decision trees"
    - "sketchygan towards diverse and realistic sketch to image synthesis"
    - "embedding visual hierarchy with deep networks for large scale visual recognition"

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)