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 subspace clustering networks"
  Abstract: We present a novel deep neural network architecture for unsupervised subspace clustering. This architecture is built upon deep auto-encoders, which non-linearly map the input data into a latent space. Our key idea is to introduce a novel self-expressive layer between the encoder and the decoder to mimic the "self-expressiveness" property that has proven effective in traditional subspace clustering. Being differentiable, our new self-expressive layer provides a simple but effective way to learn pairwise affinities between all data points through a standard back-propagation procedure. Being nonlinear, our neural-network based method is able to cluster data points having complex (often nonlinear) structures. We further propose pre-training and fine-tuning strategies that let us effectively learn the parameters of our subspace clustering networks. Our experiments show that the proposed method significantly outperforms the state-of-the-art unsupervised subspace clustering methods.

Connected through hyperedge HE-1 (induced by source paper "gatcluster self supervised gaussian attention network for image clustering"):
  with members:
    - "self supervised convolutional subspace clustering network"
    - "deep comprehensive correlation mining for image clustering"
    - "harmonious attention network for person re identification"
    - "an end to end textspotter with explicit alignment and attention"
    - "deep clustering for unsupervised learning of visual features"
    - "unsupervised representation learning by predicting image rotations"
    - "tell me where to look guided attention inference network"
    - "decidenet counting varying density crowds through attention guided detection and density estimation"

Connected through hyperedge HE-2 (induced by source paper "multi level representation learning for deep subspace clustering"):
  with members:
    - "self supervised convolutional subspace clustering network"
    - "deep spectral clustering using dual autoencoder network"
    - "neural collaborative subspace clustering"
    - "unsupervised image to image translation using domain specific variational information bound"
    - "clustergan latent space clustering in generative adversarial networks"
    - "deep multimodal subspace clustering networks"
    - "deep unsupervised clustering with gaussian mixture variational autoencoders"
    - "deep clustering via joint convolutional autoencoder embedding and relative entropy minimization"

Connected through hyperedge HE-3 (induced by source paper "structural deep clustering network"):
  with members:
    - "attributed graph clustering a deep attentional embedding approach"
    - "predict then propagate graph neural networks meet personalized pagerank"
    - "deep clustering for unsupervised learning of visual features"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "semi supervised classification with graph convolutional networks"
    - "variational deep embedding an unsupervised and generative approach to clustering"
    - "towards k means friendly spaces simultaneous deep learning and clustering"
    - "lstm based encoder decoder for multi sensor anomaly detection"

Connected through hyperedge HE-4 (induced by source paper "variational inference for deep probabilistic canonical correlation analysis"):
  with members:
    - "an introduction to variational autoencoders"
    - "deep multimodal subspace clustering networks"
    - "deep variational canonical correlation analysis"
    - "acoustic feature learning via deep variational canonical correlation analysis"
    - "adam a method for stochastic optimization"
    - "manifold relevance determination"
    - "a tutorial on spectral clustering"
    - "objective criteria for the evaluation of clustering methods"

Connected through hyperedge HE-5 (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"
    - "deep continuous clustering"
    - "unsupervised representation learning by predicting image rotations"
    - "deep clustering on the link between discriminative models and k means"

Connected through hyperedge HE-6 (induced by source paper "deep comprehensive correlation mining for image clustering"):
  with members:
    - "self supervised visual feature learning with deep neural networks a survey"
    - "differentiable linearized admm"
    - "revisiting self supervised visual representation learning"
    - "boosting self supervised learning via knowledge transfer"
    - "deep clustering for unsupervised learning of visual features"
    - "recurrent squeeze and excitation context aggregation net for single image deraining"
    - "essential tensor learning for multi view spectral clustering"
    - "unsupervised representation learning by predicting image rotations"

Connected through hyperedge HE-7 (induced by source paper "learning for multi type subspace clustering"):
  with members:
    - "motion segmentation by exploiting complementary geometric models"
    - "convolutional neural network architecture for geometric matching"
    - "multi class model fitting by energy minimization and mode seeking"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "relative camera pose estimation using convolutional neural networks"
    - "learning to find good correspondences"
    - "nonnegative matrix underapproximation for robust multiple model fitting"
    - "deep image homography estimation"

Connected through hyperedge HE-8 (induced by source paper "c2ae class conditioned auto encoder for open set recognition"):
  with members:
    - "one class convolutional neural network"
    - "active authentication using an autoencoder regularized cnn based one class classifier"
    - "deep transfer learning for multiple class novelty detection"
    - "learning deep features for one class classification"
    - "ocgan one class novelty detection using gans with constrained latent representations"
    - "dafe fd density aware feature enrichment for face detection"
    - "reducing network agnostophobia"
    - "deep clustering for unsupervised learning of visual features"

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)