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: "cnn based joint clustering and representation learning with feature drift compensation for large scale image data"
  Abstract: Given a large unlabeled set of images, how to efficiently and effectively group them into clusters based on extracted visual representations remains a challenging problem. To address this problem, we propose a convolutional neural network (CNN) to jointly solve clustering and representation learning in an iterative manner. In the proposed method, given an input image set, we first randomly pick k samples and extract their features as initial cluster centroids using the proposed CNN with an initial model pre-trained from the ImageNet dataset. Mini-batch k-means is then performed to assign cluster labels to individual input samples for a mini-batch of images randomly sampled from the input image set until all images are processed. Subsequently, the proposed CNN simultaneously updates the parameters of the proposed CNN and the centroids of image clusters iteratively based on stochastic gradient descent. We also proposed a feature drift compensation scheme to mitigate the drift error caused by feature mismatch in representation learning. Experimental results demonstrate the proposed method outperforms start-of-the-art clustering schemes in terms of accuracy and storage complexity on large-scale image sets containing millions of images.

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 "unsupervised multi task feature learning on point clouds"):
  with members:
    - "monte carlo convolution for learning on non uniformly sampled point clouds"
    - "3d point capsule networks"
    - "clustering with deep learning taxonomy and new methods"
    - "multiresolution tree networks for 3d point cloud processing"
    - "deep clustering for unsupervised learning of visual features"
    - "spidercnn deep learning on point sets with parameterized convolutional filters"
    - "ppf foldnet unsupervised learning of rotation invariant 3d local descriptors"
    - "splatnet sparse lattice networks for point cloud processing"

Connected through hyperedge HE-3 (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-4 (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"
    - "deep continuous clustering"
    - "large scale gan training for high fidelity natural image synthesis"

Connected through hyperedge HE-5 (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"
    - "deep k means jointly clustering with k means and learning representations"
    - "clustergan latent space clustering in generative adversarial networks"
    - "loss sensitive generative adversarial networks on lipschitz densities"
    - "adversarial symmetric variational autoencoder"
    - "metric learning based generative adversarial network"
    - "generalization and equilibrium in generative adversarial nets gans"

Connected through hyperedge HE-6 (induced by source paper "mixture of experts variational autoencoder for clustering and generating from similarity based representations"):
  with members:
    - "variational psom deep probabilistic clustering with self organizing maps"
    - "clustering with deep learning taxonomy and new methods"
    - "som vae interpretable discrete representation learning on time series"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "deep unsupervised clustering using mixture of autoencoders"
    - "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"

Connected through hyperedge HE-7 (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"
    - "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"
    - "joint unsupervised learning of deep representations and image clusters"

Connected through hyperedge HE-8 (induced by source paper "deep representation learning characterized by inter class separation for image clustering"):
  with members:
    - "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"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "unsupervised deep embedding for clustering analysis"
    - "adam a method for stochastic optimization"

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)