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: "omniart multi task deep learning for artistic data analysis"
  Abstract: Vast amounts of artistic data is scattered on-line from both museums and art applications. Collecting, processing and studying it with respect to all accompanying attributes is an expensive process. With a motivation to speed up and improve the quality of categorical analysis in the artistic domain, in this paper we propose an efficient and accurate method for multi-task learning with a shared representation applied in the artistic domain. We continue to show how different multi-task configurations of our method behave on artistic data and outperform handcrafted feature approaches as well as convolutional neural networks. In addition to the method and analysis, we propose a challenge like nature to the new aggregated data set with almost half a million samples and structured meta-data to encourage further research and societal engagement.

Connected through hyperedge HE-1 (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"
    - "towards k means friendly spaces simultaneous deep learning and clustering"
    - "tensorflow a system for large scale machine learning"
    - "unsupervised deep embedding for clustering analysis"

Connected through hyperedge HE-2 (induced by source paper "visual link retrieval and knowledge discovery in painting datasets"):
  with members:
    - "discovering visual patterns in art collections with spatially consistent feature learning"
    - "art2real unfolding the reality of artworks via semantically aware image to image translation"
    - "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"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "tensorflow a system for large scale machine learning"
    - "the cross depiction problem computer vision algorithms for recognising objects in artwork and in photographs"

Connected through hyperedge HE-3 (induced by source paper "hyperspherical prototype networks"):
  with members:
    - "prototypical metric transfer learning for continuous speech keyword spotting with limited training data"
    - "infinite mixture prototypes for few shot learning"
    - "transferrable prototypical networks for unsupervised domain adaptation"
    - "deep learning on small datasets without pre training using cosine loss"
    - "decoupled networks"
    - "fix your classifier the marginal value of training the last weight layer"
    - "cosface large margin cosine loss for deep face recognition"
    - "robust classification with convolutional prototype learning"

Connected through hyperedge HE-4 (induced by source paper "discovering visual patterns in art collections with spatially consistent feature learning"):
  with members:
    - "can computers create art"
    - "neighbourhood consensus networks"
    - "weakly supervised object detection in artworks"
    - "can creative adversarial networks generating art by learning about styles and deviating from style norms"
    - "fine tuning cnn image retrieval with no human annotation"
    - "bam the behance artistic media dataset for recognition beyond photography"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "detecting people in artwork with cnns"

Connected through hyperedge HE-5 (induced by source paper "learning task relatedness in multi task learning for images in context"):
  with members:
    - "taskonomy disentangling task transfer learning"
    - "gnas a greedy neural architecture search method for multi attribute learning"
    - "sketchparse towards rich descriptions for poorly drawn sketches using multi task hierarchical deep networks"
    - "an overview of multi task learning in deep neural networks"
    - "deep cross residual learning for multitask visual recognition"
    - "distributed multi task relationship learning"
    - "deep multi task representation learning a tensor factorisation approach"
    - "ubernet training a universal convolutional neural network for low mid and high level vision using diverse datasets and limited memory"

Connected through hyperedge HE-6 (induced by source paper "hyperlearn a distributed approach for representation learning in datasets with many modalities"):
  with members:
    - "social anchor unit graph regularized tensor completion for large scale image retagging"
    - "videos as space time region graphs"
    - "geometric matrix completion with recurrent multi graph neural networks"
    - "geometric deep learning going beyond euclidean data"
    - "learning shape correspondence with anisotropic convolutional neural networks"
    - "semi supervised classification with graph convolutional networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "socializing the semantic gap a comparative survey on image tag assignment refinement and retrieval"

Connected through hyperedge HE-7 (induced by source paper "historical and modern features for buddha statue classification"):
  with members:
    - "context aware embeddings for automatic art analysis"
    - "how to read paintings semantic art understanding with multi modal retrieval"
    - "joint 3d face reconstruction and dense alignment with position map regression network"
    - "the shape of art history in the eyes of the machine"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "node2vec scalable feature learning for networks"
    - "deep residual learning for image recognition"
    - "large scale classification of fine art paintings learning the right metric on the right feature"

Connected through hyperedge HE-8 (induced by source paper "weakly supervised object detection in artworks"):
  with members:
    - "do better imagenet models transfer better"
    - "cross domain weakly supervised object detection through progressive domain adaptation"
    - "an implementation of faster rcnn with study for region sampling"
    - "multiple instance learning a survey of problem characteristics and applications"
    - "bam the behance artistic media dataset for recognition beyond photography"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "deeper broader and artier domain generalization"
    - "weakly supervised object localization with multi fold multiple instance 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.MM(Multimedia)