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 continuous clustering"
  Abstract: Clustering high-dimensional datasets is hard because interpoint distances become less informative in high-dimensional spaces. We present a clustering algorithm that performs nonlinear dimensionality reduction and clustering jointly. The data is embedded into a lower-dimensional space by a deep autoencoder. The autoencoder is optimized as part of the clustering process. The resulting network produces clustered data. The presented approach does not rely on prior knowledge of the number of ground-truth clusters. Joint nonlinear dimensionality reduction and clustering are formulated as optimization of a global continuous objective. We thus avoid discrete reconfigurations of the objective that characterize prior clustering algorithms. Experiments on datasets from multiple domains demonstrate that the presented algorithm outperforms state-of-the-art clustering schemes, including recent methods that use deep networks.

Connected through hyperedge HE-1 (induced by source paper "deep clustering with a dynamic autoencoder from reconstruction towards centroids construction"):
  with members:
    - "adversarial deep embedded clustering on a better trade off between feature randomness and feature drift"
    - "revisiting self supervised visual representation learning"
    - "understanding and improving interpolation in autoencoders via an adversarial regularizer"
    - "deep clustering for unsupervised learning of visual features"
    - "deep discriminative latent space for clustering"
    - "unsupervised representation learning by predicting image rotations"
    - "deep clustering on the link between discriminative models and k means"
    - "representation learning with contrastive predictive coding"

Connected through hyperedge HE-2 (induced by source paper "adversarial deep embedded clustering on a better trade off between feature randomness and feature drift"):
  with members:
    - "deep clustering with a dynamic autoencoder from reconstruction towards centroids construction"
    - "efficient parameter free clustering using first neighbor relations"
    - "deep clustering with a dynamic autoencoder"
    - "understanding and improving interpolation in autoencoders via an adversarial regularizer"
    - "deep clustering for unsupervised learning of visual features"
    - "deep discriminative latent space for clustering"
    - "large scale gan training for high fidelity natural image synthesis"
    - "deep clustering on the link between discriminative models and k means"

Connected through hyperedge HE-3 (induced by source paper "deep discriminative clustering analysis"):
  with members:
    - "variational wasserstein clustering"
    - "deep clustering for unsupervised learning of visual features"
    - "clustergan latent space clustering in generative adversarial networks"
    - "relational inductive biases deep learning and graph networks"
    - "learning markov clustering networks for scene text detection"
    - "hierarchical clustering with structural constraints"
    - "deep unsupervised clustering with gaussian mixture variational autoencoders"
    - "learning from video and text via large scale discriminative clustering"

Connected through hyperedge HE-4 (induced by source paper "elastic infogan unsupervised disentangled representation learning in imbalanced data"):
  with members:
    - "finegan unsupervised hierarchical disentanglement for fine grained object generation and discovery"
    - "exploring the limits of weakly supervised pretraining"
    - "domain adaptation for semantic segmentation via class balanced self training"
    - "a systematic study of the class imbalance problem in convolutional neural networks"
    - "unsupervised learning of disentangled representations from video"
    - "class rectification hard mining for imbalanced deep learning"
    - "stackgan realistic image synthesis with stacked generative adversarial networks"
    - "disentangling factors of variation by mixing them"

Connected through hyperedge HE-5 (induced by source paper "efficient parameter free clustering using first neighbor relations"):
  with members:
    - "self supervised learning of face representations for video face clustering"
    - "deep clustering for unsupervised learning of visual features"
    - "multi view low rank sparse subspace clustering"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "clustering millions of faces by identity"
    - "hierarchical clustering via spreading metrics"
    - "approximate hierarchical clustering via sparsest cut and spreading metrics"
    - "variational deep embedding an unsupervised and generative approach to clustering"

Connected through hyperedge HE-6 (induced by source paper "deep spectral clustering using dual autoencoder network"):
  with members:
    - "coupled cyclegan unsupervised hashing network for cross modal retrieval"
    - "deep discriminative latent space for clustering"
    - "clustergan latent space clustering in generative adversarial networks"
    - "self supervised adversarial hashing networks for cross modal retrieval"
    - "deep unsupervised clustering with gaussian mixture variational autoencoders"
    - "deep subspace clustering networks"
    - "deep clustering via joint convolutional autoencoder embedding and relative entropy minimization"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"

Connected through hyperedge HE-7 (induced by source paper "deep discriminative latent space for clustering"):
  with members:
    - "deep subspace clustering networks"
    - "deep clustering via joint convolutional autoencoder embedding and relative entropy minimization"
    - "discriminatively boosted image clustering with fully convolutional auto encoders"
    - "real time single image and video super resolution using an efficient sub pixel convolutional neural network"
    - "variational deep embedding an unsupervised and generative approach to clustering"
    - "towards k means friendly spaces simultaneous deep learning and clustering"
    - "joint unsupervised learning of deep representations and image clusters"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-8 (induced by source paper "deep density based image clustering"):
  with members:
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "towards k means friendly spaces simultaneous deep learning and clustering"
    - "joint unsupervised learning of deep representations and image clusters"
    - "unsupervised deep embedding for clustering analysis"
    - "deep learning with nonparametric clustering"
    - "representation learning a review and new perspectives"

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)