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: "context aware embeddings for automatic art analysis"
  Abstract: Automatic art analysis aims to classify and retrieve artistic representations from a collection of images by using computer vision and machine learning techniques. In this work, we propose to enhance visual representations from neural networks with contextual artistic information. Whereas visual representations are able to capture information about the content and the style of an artwork, our proposed context-aware embeddings additionally encode relationships between different artistic attributes, such as author, school, or historical period. We design two different approaches for using context in automatic art analysis. In the first one, contextual data is obtained through a multi-task learning model, in which several attributes are trained together to find visual relationships between elements. In the second approach, context is obtained through an art-specific knowledge graph, which encodes relationships between artistic attributes. An exhaustive evaluation of both of our models in several art analysis problems, such as author identification, type classification, or cross-modal retrieval, show that performance is improved by up to 7.3% in art classification and 37.24% in retrieval when context-aware embeddings are used.

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"
    - "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"
    - "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 "historical and modern features for buddha statue classification"):
  with members:
    - "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"
    - "omniart multi task deep learning for artistic data analysis"
    - "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-3 (induced by source paper "understanding art through multi modal retrieval in paintings"):
  with members:
    - "a style aware content loss for real time hd style transfer"
    - "how to read paintings semantic art understanding with multi modal retrieval"
    - "starspace embed all the things"
    - "node2vec scalable feature learning for networks"
    - "vqa visual question answering"
    - "deep residual learning for image recognition"
    - "microsoft coco common objects in context"
    - "recognizing image style"

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)