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: "toward automated discovery of artistic influence"
  Abstract: Considering the huge amount of art pieces that exist, there is valuable information to be discovered. Examining a painting, an expert can determine its style, genre, and the time period that the painting belongs. One important task for art historians is to find influences and connections between artists. Is influence a task that a computer can measure? The contribution of this paper is in exploring the problem of computer-automated suggestion of influences between artists, a problem that was not addressed before in a general setting. We first present a comparative study of different classification methodologies for the task of fine-art style classification. A two-level comparative study is performed for this classification problem. The first level reviews the performance of discriminative vs. generative models, while the second level touches the features aspect of the paintings and compares semantic-level features vs. low-level and intermediate-level features present in the painting. Then, we investigate the question "Who influenced this artist?" by looking at his masterpieces and comparing them to others. We pose this interesting question as a knowledge discovery problem. For this purpose, we investigated several painting-similarity and artist-similarity measures. As a result, we provide a visualization of artists (Map of Artists) based on the similarity between their works

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"
    - "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"

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"
    - "omniart multi task deep learning for artistic data analysis"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-3 (induced by source paper "deep learning architect classification for architectural design through the eye of artificial intelligence"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "treepedia 2 0 applying deep learning for large scale quantification of urban tree cover"
    - "the shape of art history in the eyes of the machine"
    - "learning transferable architectures for scalable image recognition"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"
    - "neural machine translation by jointly learning to align and translate"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

Connected through hyperedge HE-4 (induced by source paper "draw deep networks for recognizing styles of artists who illustrate children s books"):
  with members:
    - "perceptual losses for real time style transfer and super resolution"
    - "a learned representation for artistic style"
    - "seeing behind the camera identifying the authorship of a photograph"
    - "understanding neural networks through deep visualization"
    - "a neural algorithm of artistic style"
    - "the cross depiction problem computer vision algorithms for recognising objects in artwork and in photographs"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"

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)