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 k means jointly clustering with k means and learning representations"
  Abstract: We study in this paper the problem of jointly clustering and learning representations. As several previous studies have shown, learning representations that are both faithful to the data to be clustered and adapted to the clustering algorithm can lead to better clustering performance, all the more so that the two tasks are performed jointly. We propose here such an approach for $k$-Means clustering based on a continuous reparametrization of the objective function that leads to a truly joint solution. The behavior of our approach is illustrated on various datasets showing its efficacy in learning representations for objects while clustering them.

Connected through hyperedge HE-1 (induced by source paper "dhog deep hierarchical object grouping"):
  with members:
    - "on mutual information maximization for representation learning"
    - "deep comprehensive correlation mining for image clustering"
    - "randaugment practical automated data augmentation with a reduced search space"
    - "randaugment practical data augmentation with no separate search"
    - "fast autoaugment"
    - "learning representations by maximizing mutual information across views"
    - "cinic 10 is not imagenet or cifar 10"
    - "deep clustering for unsupervised learning of visual features"

Connected through hyperedge HE-2 (induced by source paper "label removed generative adversarial networks incorporating with k means"):
  with members:
    - "gaussian mixture generative adversarial networks for diverse datasets and the unsupervised clustering of images"
    - "clustering with deep learning taxonomy and new methods"
    - "clustergan latent space clustering in generative adversarial networks"
    - "loss sensitive generative adversarial networks on lipschitz densities"
    - "adversarial symmetric variational autoencoder"
    - "cnn based joint clustering and representation learning with feature drift compensation for large scale image data"
    - "metric learning based generative adversarial network"
    - "generalization and equilibrium in generative adversarial nets gans"

Connected through hyperedge HE-3 (induced by source paper "structure learning with similarity preserving"):
  with members:
    - "multi view multiple clustering"
    - "similarity learning via kernel preserving embedding"
    - "deep subspace clustering networks"
    - "twin learning for similarity and clustering a unified kernel approach"
    - "constructing the l2 graph for robust subspace learning and subspace clustering"
    - "top n recommender system via matrix completion"
    - "unsupervised deep embedding for clustering analysis"
    - "scalable sparse subspace clustering by orthogonal matching pursuit"

Connected through hyperedge HE-4 (induced by source paper "differentiable deep clustering with cluster size constraints"):
  with members:
    - "deep multi class learning from label proportions"
    - "improving gans using optimal transport"
    - "deep clustering via joint convolutional autoencoder embedding and relative entropy minimization"
    - "towards k means friendly spaces simultaneous deep learning and clustering"
    - "joint unsupervised learning of deep representations and image clusters"
    - "semi supervised learning with deep generative models"
    - "representation learning a review and new perspectives"
    - "learning probability measures with respect to optimal transport metrics"

Connected through hyperedge HE-5 (induced by source paper "spectral clustering via ensemble deep autoencoder learning sc edae"):
  with members:
    - "on the convergence of adam and beyond"
    - "deep subspace clustering networks"
    - "fast spectral clustering using autoencoders and landmarks"
    - "pca initialized deep neural networks applied to document image analysis"
    - "towards k means friendly spaces simultaneous deep learning and clustering"
    - "unsupervised deep embedding for clustering analysis"
    - "generalized denoising auto encoders as generative models"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-6 (induced by source paper "a binary optimization approach for constrained k means clustering"):
  with members:
    - "towards k means friendly spaces simultaneous deep learning and clustering"

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)