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: "discriminatively boosted image clustering with fully convolutional auto encoders"
  Abstract: Traditional image clustering methods take a two-step approach, feature learning and clustering, sequentially. However, recent research results demonstrated that combining the separated phases in a unified framework and training them jointly can achieve a better performance. In this paper, we first introduce fully convolutional auto-encoders for image feature learning and then propose a unified clustering framework to learn image representations and cluster centers jointly based on a fully convolutional auto-encoder and soft $k$-means scores. At initial stages of the learning procedure, the representations extracted from the auto-encoder may not be very discriminative for latter clustering. We address this issue by adopting a boosted discriminative distribution, where high score assignments are highlighted and low score ones are de-emphasized. With the gradually boosted discrimination, clustering assignment scores are discriminated and cluster purities are enlarged. Experiments on several vision benchmark datasets show that our methods can achieve a state-of-the-art performance.

Connected through hyperedge HE-1 (induced by source paper "deep amortized clustering"):
  with members:
    - "multi class classification without multi class labels"
    - "are few shot learning benchmarks too simple"
    - "set transformer a framework for attention based permutation invariant neural networks"
    - "clustering with deep learning taxonomy and new methods"
    - "conditional neural processes"
    - "deep sets"
    - "invariant information clustering for unsupervised image classification and segmentation"
    - "user experience of the costar system for instruction of collaborative robots"

Connected through hyperedge HE-2 (induced by source paper "an image clustering auto encoder based on predefined evenly distributed class centroids and mmd distance"):
  with members:
    - "local deep feature alignment for unsupervised dimension reduction"
    - "a classification supervised auto encoder based on predefined evenly distributed class centroids"
    - "cnn based joint clustering and representation learning with feature drift compensation for large scale image data"
    - "deep clustering via joint convolutional autoencoder embedding and relative entropy minimization"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "novel tracking approach based on fully unsupervised disentanglement of the geometrical factors of variation"):
  with members:
    - "disentangling factors of variation using few labels"
    - "scalable deep convolutional neural networks for sparse locally dense liquid argon time projection chamber data"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "variational inference of disentangled latent concepts from unlabeled observations"
    - "u net convolutional networks for biomedical image segmentation"
    - "spatial transformer networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "unsupervised continual learning and self taught associative memory hierarchies"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "re evaluating continual learning scenarios a categorization and case for strong baselines"
    - "realistic evaluation of deep semi supervised learning algorithms"
    - "deep predictive coding networks for video prediction and unsupervised learning"
    - "adam a method for stochastic optimization"
    - "semi supervised learning with deep generative models"

Connected through hyperedge HE-5 (induced by source paper "unsupervised robotic sorting towards autonomous decision making robots"):
  with members:
    - "semantically meaningful view selection"
    - "unsupervised joint mining of deep features and image labels for large scale radiology image categorization and scene recognition"
    - "iterative views agreement an iterative low rank based structured optimization method to multi view spectral clustering"
    - "xception deep learning with depthwise separable convolutions"
    - "joint unsupervised learning of deep representations and image clusters"
    - "deep residual learning for image recognition"
    - "rethinking the inception architecture for computer vision"
    - "multimodal deep learning for robust rgb d object recognition"

Connected through hyperedge HE-6 (induced by source paper "clustering with deep learning taxonomy and new methods"):
  with members:
    - "cnn based joint clustering and representation learning with feature drift compensation for large scale image data"
    - "deep clustering via joint convolutional autoencoder embedding and relative entropy minimization"
    - "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"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "unsupervised deep embedding for clustering analysis"

Connected through hyperedge HE-7 (induced by source paper "deep discriminative latent space for clustering"):
  with members:
    - "deep continuous clustering"
    - "deep subspace clustering networks"
    - "deep clustering via joint convolutional autoencoder embedding and relative entropy minimization"
    - "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 "recurrent deep embedding networks for genotype clustering and ethnicity prediction"):
  with members:
    - "clustering with deep learning taxonomy and new methods"
    - "unsupervised deep embedding for clustering analysis"
    - "adadelta an adaptive learning rate method"

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)