human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "label removed generative adversarial networks incorporating with k means"):
  Abstract: Generative Adversarial Networks (GANs) have achieved great success in generating realistic images. Most of these are conditional models, although acquisition of class labels is expensive and time-consuming in practice. To reduce the dependence on labeled data, we propose an un-conditional generative adversarial model, called K-Means-GAN (KM-GAN), which incorporates the idea of updating centers in K-Means into GANs. Specifically, we redesign the framework of GANs by applying K-Means on the features extracted from the discriminator. With obtained labels from K-Means, we propose new objective functions from the perspective of deep metric learning (DML). Distinct from previous works, the discriminator is treated as a feature extractor rather than a classifier in KM-GAN, meanwhile utilization of K-Means makes features of the discriminator more representative. Experiments are conducted on various datasets, such as MNIST, Fashion-10, CIFAR-10 and CelebA, and show that the quality of samples generated by KM-GAN is comparable to some conditional generative adversarial models.

Includes members:
  Member "gaussian mixture generative adversarial networks for diverse datasets and the unsupervised clustering of images":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "unsupervised k modal styled content generation")
  Member "clustering with deep learning taxonomy and new methods":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "memory based graph networks")
  Member "deep k means jointly clustering with k means and learning representations":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "dhog deep hierarchical object grouping")
  Member "clustergan latent space clustering in generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multi level representation learning for deep subspace clustering")
  Member "loss sensitive generative adversarial networks on lipschitz densities":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "adversarial symmetric variational autoencoder":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "double backpropagation for training autoencoders against adversarial attack")
  Member "cnn based joint clustering and representation learning with feature drift compensation for large scale image data":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "gatcluster self supervised gaussian attention network for image clustering")
  Member "metric learning based generative adversarial network":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "opengan open set generative adversarial networks")

Question: Please tell me which class the source paper 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)