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: "graph degree linkage agglomerative clustering on a directed graph"
  Abstract: This paper proposes a simple but effective graph-based agglomerative algorithm, for clustering high-dimensional data. We explore the different roles of two fundamental concepts in graph theory, indegree and outdegree, in the context of clustering. The average indegree reflects the density near a sample, and the average outdegree characterizes the local geometry around a sample. Based on such insights, we define the affinity measure of clusters via the product of average indegree and average outdegree. The product-based affinity makes our algorithm robust to noise. The algorithm has three main advantages: good performance, easy implementation, and high computational efficiency. We test the algorithm on two fundamental computer vision problems: image clustering and object matching. Extensive experiments demonstrate that it outperforms the state-of-the-arts in both applications.

Connected through hyperedge HE-1 (induced by source paper "clustering by directly disentangling latent space"):
  with members:
    - "a two step disentanglement method"
    - "dominant set clustering and pooling for multi view 3d object recognition"
    - "y autoencoders disentangling latent representations via sequential encoding"
    - "deep spectral clustering using dual autoencoder network"
    - "from variational to deterministic autoencoders"
    - "adversarial deep embedded clustering on a better trade off between feature randomness and feature drift"
    - "deep clustering for unsupervised learning of visual features"
    - "clustergan latent space clustering in generative adversarial networks"

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"
    - "discriminatively boosted image clustering with fully convolutional auto encoders"
    - "variational deep embedding an unsupervised and generative approach to clustering"
    - "towards k means friendly spaces simultaneous deep learning and clustering"

Connected through hyperedge HE-3 (induced by source paper "recal reuse of established cnn classifer apropos unsupervised learning paradigm"):
  with members:
    - "deep clustering via joint convolutional autoencoder embedding and relative entropy minimization"
    - "joint unsupervised learning of deep representations and image clusters"
    - "deep residual learning for image recognition"
    - "learning a task specific deep architecture for clustering"
    - "unsupervised deep embedding for clustering analysis"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-4 (induced by source paper "sampling clustering"):
  with members:
    - "border peeling clustering"

Connected through hyperedge HE-5 (induced by source paper "clustergan latent space clustering in generative adversarial networks"):
  with members:
    - "inverting the generator of a generative adversarial network ii"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "deep subspace clustering networks"
    - "improved training of wasserstein gans"
    - "the robust manifold defense adversarial training using generative models"
    - "precise recovery of latent vectors from generative adversarial networks"
    - "adversarial feature learning"
    - "towards k means friendly spaces simultaneous deep learning and clustering"

Connected through hyperedge HE-6 (induced by source paper "deep continuous clustering"):
  with members:
    - "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"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "unsupervised deep embedding for clustering analysis"
    - "adam a method for stochastic optimization"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-7 (induced by source paper "deep clustering via joint convolutional autoencoder embedding and relative entropy minimization"):
  with members:
    - "semantic image inpainting with deep generative models"
    - "semantic image inpainting with perceptual and contextual losses"
    - "joint unsupervised learning of deep representations and image clusters"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "learning a task specific deep architecture for clustering"
    - "unsupervised deep embedding for clustering analysis"
    - "semi supervised learning with ladder networks"
    - "scalable sparse subspace clustering by orthogonal matching pursuit"

Connected through hyperedge HE-8 (induced by source paper "merge or not learning to group faces via imitation learning"):
  with members:
    - "clustering millions of faces by identity"
    - "ms celeb 1m a dataset and benchmark for large scale face recognition"
    - "facenet a unified embedding for face recognition and clustering"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "rethinking the inception architecture for computer vision"
    - "deeply learned face representations are sparse selective and robust"
    - "a reduction of imitation learning and structured prediction to no regret online learning"

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)