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: "towards k means friendly spaces simultaneous deep learning and clustering"
  Abstract: Most learning approaches treat dimensionality reduction (DR) and clustering separately (i.e., sequentially), but recent research has shown that optimizing the two tasks jointly can substantially improve the performance of both. The premise behind the latter genre is that the data samples are obtained via linear transformation of latent representations that are easy to cluster; but in practice, the transformation from the latent space to the data can be more complicated. In this work, we assume that this transformation is an unknown and possibly nonlinear function. To recover the `clustering-friendly' latent representations and to better cluster the data, we propose a joint DR and K-means clustering approach in which DR is accomplished via learning a deep neural network (DNN). The motivation is to keep the advantages of jointly optimizing the two tasks, while exploiting the deep neural network's ability to approximate any nonlinear function. This way, the proposed approach can work well for a broad class of generative models. Towards this end, we carefully design the DNN structure and the associated joint optimization criterion, and propose an effective and scalable algorithm to handle the formulated optimization problem. Experiments using different real datasets are employed to showcase the effectiveness of the proposed approach.

Connected through hyperedge HE-1 (induced by source paper "automatically discovering and learning new visual categories with ranking statistics"):
  with members:
    - "learning to discover novel visual categories via deep transfer clustering"
    - "multi class classification without multi class labels"
    - "semi supervised learning with scarce annotations"
    - "revisiting self supervised visual representation learning"
    - "a survey on deep transfer learning"
    - "unsupervised representation learning by predicting image rotations"
    - "realistic evaluation of deep semi supervised learning algorithms"
    - "memory aware synapses learning what not to forget"

Connected through hyperedge HE-2 (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-3 (induced by source paper "unsupervised multiple person tracking using autoencoder based lifted multicuts"):
  with members:
    - "tracking without bells and whistles"
    - "famnet joint learning of feature affinity and multi dimensional assignment for online multiple object tracking"
    - "multi object tracking with multiple cues and switcher aware classification"
    - "online multi object tracking with historical appearance matching and scene adaptive detection filtering"
    - "unsupervised cross dataset person re identification by transfer learning of spatial temporal patterns"
    - "trajectory factory tracklet cleaving and re connection by deep siamese bi gru for multiple object tracking"
    - "exploit the connectivity multi object tracking with trackletnet"
    - "unsupervised person re identification by deep learning tracklet association"

Connected through hyperedge HE-4 (induced by source paper "deep convolutional embedding for digitized painting clustering"):
  with members:
    - "discovering visual patterns in art collections with spatially consistent feature learning"
    - "context aware embeddings for automatic art analysis"
    - "weakly supervised object detection in artworks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "bam the behance artistic media dataset for recognition beyond photography"
    - "omniart multi task deep learning for artistic data analysis"
    - "tensorflow a system for large scale machine learning"
    - "unsupervised deep embedding for clustering analysis"

Connected through hyperedge HE-5 (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"
    - "deep subspace clustering networks"
    - "semi supervised classification with graph convolutional networks"
    - "variational deep embedding an unsupervised and generative approach to clustering"
    - "lstm based encoder decoder for multi sensor anomaly detection"

Connected through hyperedge HE-6 (induced by source paper "unsupervised deep clustering for predictive texture pattern discovery in medical images"):
  with members:
    - "unsupervised segmentation of 3d medical images based on clustering and deep representation learning"
    - "deep clustering for unsupervised learning of visual features"
    - "unsupervised joint mining of deep features and image labels for large scale radiology image categorization and scene recognition"
    - "joint unsupervised learning of deep representations and image clusters"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-7 (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-8 (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"

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)