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: "md gan multi discriminator generative adversarial networks for distributed datasets"
  Abstract: A recent technical breakthrough in the domain of machine learning is the discovery and the multiple applications of Generative Adversarial Networks (GANs). Those generative models are computationally demanding, as a GAN is composed of two deep neural networks, and because it trains on large datasets. A GAN is generally trained on a single server. #R##N#In this paper, we address the problem of distributing GANs so that they are able to train over datasets that are spread on multiple workers. MD-GAN is exposed as the first solution for this problem: we propose a novel learning procedure for GANs so that they fit this distributed setup. We then compare the performance of MD-GAN to an adapted version of Federated Learning to GANs, using the MNIST and CIFAR10 datasets. MD-GAN exhibits a reduction by a factor of two of the learning complexity on each worker node, while providing better performances than federated learning on both datasets. We finally discuss the practical implications of distributing GANs.

Connected through hyperedge HE-1 (induced by source paper "brainstorming generative adversarial networks bgans towards multi agent generative models with distributed private datasets"):
  with members:
    - "generative adversarial networks for distributed intrusion detection in the internet of things"
    - "decentralized learning of generative adversarial networks from multi client non iid data"
    - "multi agent diverse generative adversarial networks"
    - "precomputed real time texture synthesis with markovian generative adversarial networks"
    - "generative adversarial text to image synthesis"
    - "generating videos with scene dynamics"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-2 (induced by source paper "one shot image to image translation via part global learning with a multi adversarial framework"):
  with members:
    - "generative adversarial network with multi branch discriminator for cross species image to image translation"
    - "semantic image retrieval by uniting deep neural networks and cognitive architectures"
    - "a multi discriminator cyclegan for unsupervised non parallel speech domain adaptation"
    - "pros and cons of gan evaluation measures"
    - "multimodal unsupervised image to image translation"
    - "one shot unsupervised cross domain translation"
    - "improving shape deformation in unsupervised image to image translation"
    - "few shot adaptation for multimedia semantic indexing"

Connected through hyperedge HE-3 (induced by source paper "decentralized learning of generative adversarial networks from non iid data"):
  with members:
    - "towards federated learning at scale system design"
    - "cgans with projection discriminator"
    - "cinic 10 is not imagenet or cifar 10"
    - "demystifying parallel and distributed deep learning an in depth concurrency analysis"
    - "federated learning strategies for improving communication efficiency"
    - "spectral normalization for generative adversarial networks"
    - "communication efficient on device machine learning federated distillation and augmentation under non iid private data"
    - "how to backdoor federated learning"

Connected through hyperedge HE-4 (induced by source paper "generative adversarial networks for distributed intrusion detection in the internet of things"):
  with members:
    - "a vision of 6g wireless systems applications trends technologies and open research problems"
    - "efficient gan based anomaly detection"
    - "mdgan boosting anomaly detection using multi discriminator generative adversarial networks"
    - "anomaly detection with generative adversarial networks for multivariate time series"
    - "deep learning for signal authentication and security in massive internet of things systems"
    - "a tutorial on uavs for wireless networks applications challenges and open problems"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "abnormal event detection in videos using generative adversarial nets"

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)