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: "emnist an extension of mnist to handwritten letters"
  Abstract: The MNIST dataset has become a standard benchmark for learning, classification and computer vision systems. Contributing to its widespread adoption are the understandable and intuitive nature of the task, its relatively small size and storage requirements and the accessibility and ease-of-use of the database itself. The MNIST database was derived from a larger dataset known as the NIST Special Database 19 which contains digits, uppercase and lowercase handwritten letters. This paper introduces a variant of the full NIST dataset, which we have called Extended MNIST (EMNIST), which follows the same conversion paradigm used to create the MNIST dataset. The result is a set of datasets that constitute a more challenging classification tasks involving letters and digits, and that shares the same image structure and parameters as the original MNIST task, allowing for direct compatibility with all existing classifiers and systems. Benchmark results are presented along with a validation of the conversion process through the comparison of the classification results on converted NIST digits and the MNIST digits.

Connected through hyperedge HE-1 (induced by source paper "anomalous instance detection in deep learning a survey"):
  with members:
    - "unmask adversarial detection and defense through robust feature alignment"
    - "a research agenda dynamic models to defend against correlated attacks"
    - "analysis of confident classifiers for out of distribution detection"
    - "adversarial attacks on time series"
    - "adversarial attacks on deep neural networks for time series classification"
    - "mimicgan robust projection onto image manifolds with corruption mimicking"
    - "analyzing adversarial attacks against deep learning for intrusion detection in iot networks"
    - "deep learning for anomaly detection a survey"

Connected through hyperedge HE-2 (induced by source paper "matchinggan matching based few shot image generation"):
  with members:
    - "figr few shot image generation with reptile"
    - "revisiting local descriptor based image to class measure for few shot learning"
    - "few shot unsupervised image to image translation"
    - "cgans with projection discriminator"
    - "on first order meta learning algorithms"
    - "spectral normalization for generative adversarial networks"
    - "meta transfer learning for few shot learning"
    - "an empirical study on evaluation metrics of generative adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "a deep unsupervised feature learning spiking neural network with binarized classification layers for emnist classification"):
  with members:
    - "a spiking network for inference of relations trained with neuromorphic backpropagation"
    - "deep convolutional spiking neural networks for image classification"
    - "temporal coding in spiking neural networks with alpha synaptic function"
    - "s4nn temporal backpropagation for spiking neural networks with one spike per neuron"
    - "towards scalable efficient and accurate deep spiking neural networks with backward residual connections stochastic softmax and hybridization"
    - "spyketorch efficient simulation of convolutional spiking neural networks with at most one spike per neuron"
    - "dendritic inspired processing enables bio plausible stdp in compound binary synapses"
    - "hybrid macro micro level backpropagation for training deep spiking neural networks"

Connected through hyperedge HE-4 (induced by source paper "simex express prediction of inter dataset similarity by a fleet of autoencoders"):
  with members:
    - "adversarial discriminative domain adaptation"
    - "the effectiveness of data augmentation in image classification using deep learning"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "borrowing treasures from the wealthy deep transfer learning through selective joint fine tuning"
    - "attribute2image conditional image generation from visual attributes"
    - "learning transferable features with deep adaptation networks"
    - "learning to generate images with perceptual similarity metrics"
    - "generating images from captions with attention"

Connected through hyperedge HE-5 (induced by source paper "disentanglement by nonlinear ica with general incompressible flow networks gin"):
  with members:
    - "variational autoencoder with truncated mixture of gaussians for functional connectivity analysis"
    - "flow improving flow based generative models with variational dequantization and architecture design"
    - "guided image generation with conditional invertible neural networks"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "isolating sources of disentanglement in variational autoencoders"
    - "i revnet deep invertible networks"
    - "deep unsupervised clustering with gaussian mixture variational autoencoders"
    - "density estimation using real nvp"

Connected through hyperedge HE-6 (induced by source paper "information condensing active learning"):
  with members:
    - "batchbald efficient and diverse batch acquisition for deep bayesian active learning"
    - "understanding the limitations of variational mutual information estimators"
    - "deep batch active learning by diverse uncertain gradient lower bounds"
    - "adversarial active learning for deep networks a margin based approach"
    - "discriminative active learning"
    - "deep bayesian active learning with image data"
    - "deep active learning over the long tail"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "manifold for machine learning assurance"):
  with members:
    - "input prioritization for testing neural networks"
    - "diagnosing and enhancing vae models"
    - "machine learning testing survey landscapes and horizons"
    - "deepgauge multi granularity testing criteria for deep learning systems"
    - "guiding deep learning system testing using surprise adequacy"
    - "deepxplore automated whitebox testing of deep learning systems"
    - "autoencoding beyond pixels using a learned similarity metric"
    - "representation learning a review and new perspectives"

Connected through hyperedge HE-8 (induced by source paper "distributed sketching methods for privacy preserving regression"):
  with members:
    - "occupy the cloud distributed computing for the 99"
    - "randomized sketches of convex programs with sharp guarantees"
    - "sketching as a tool for numerical linear algebra"
    - "osnap faster numerical linear algebra algorithms via sparser subspace embeddings"
    - "randomized algorithms for matrices and data"
    - "faster least squares approximation"

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)