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: "bam the behance artistic media dataset for recognition beyond photography"
  Abstract: Computer vision systems are designed to work well within the context of everyday photography. However, artists often render the world around them in ways that do not resemble photographs. Artwork produced by people is not constrained to mimic the physical world, making it more challenging for machines to recognize. #R##N#This work is a step toward teaching machines how to categorize images in ways that are valuable to humans. First, we collect a large-scale dataset of contemporary artwork from Behance, a website containing millions of portfolios from professional and commercial artists. We annotate Behance imagery with rich attribute labels for content, emotions, and artistic media. Furthermore, we carry out baseline experiments to show the value of this dataset for artistic style prediction, for improving the generality of existing object classifiers, and for the study of visual domain adaptation. We believe our Behance Artistic Media dataset will be a good starting point for researchers wishing to study artistic imagery and relevant problems.

Connected through hyperedge HE-1 (induced by source paper "self guided adaptation progressive representation alignment for domain adaptive object detection"):
  with members:
    - "self training and adversarial background regularization for unsupervised domain adaptive one stage object detection"
    - "towards universal object detection by domain attention"
    - "exploring object relation in mean teacher for cross domain detection"
    - "progressive domain adaptation for object detection"
    - "invariance matters exemplar memory for domain adaptive person re identification"
    - "diversify and match a domain adaptive representation learning paradigm for object detection"
    - "semantic foggy scene understanding with synthetic data"
    - "cross domain weakly supervised object detection through progressive domain adaptation"

Connected through hyperedge HE-2 (induced by source paper "a content transformation block for image style transfer"):
  with members:
    - "adjustable real time style transfer"
    - "artistic style transfer for videos and spherical images"
    - "a style aware content loss for real time hd style transfer"
    - "generative adversarial networks an overview"
    - "stable and controllable neural texture synthesis and style transfer using histogram losses"
    - "diversified texture synthesis with feed forward networks"
    - "characterizing and improving stability in neural style transfer"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "neural architecture search for deep image prior"):
  with members:
    - "singan learning a generative model from a single natural image"
    - "autogan neural architecture search for generative adversarial networks"
    - "double dip unsupervised image decomposition via coupled deep image priors"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "searching for efficient multi scale architectures for dense image prediction"
    - "progressive neural architecture search"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "large scale evolution of image classifiers"

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"
    - "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-5 (induced by source paper "sketchformer transformer based representation for sketched structure"):
  with members:
    - "livesketch query perturbations for guided sketch based visual search"
    - "transformer xl attentive language models beyond a fixed length context"
    - "doodle to search practical zero shot sketch based image retrieval"
    - "image transformer"
    - "learning deep sketch abstraction"
    - "sketchmate deep hashing for million scale human sketch retrieval"
    - "learning to sketch with shortcut cycle consistency"
    - "sketch r2cnn an attentive network for vector sketch recognition"

Connected through hyperedge HE-6 (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"
    - "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"
    - "the cross depiction problem computer vision algorithms for recognising objects in artwork and in photographs"

Connected through hyperedge HE-7 (induced by source paper "towards shape biased unsupervised representation learning for domain generalization"):
  with members:
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "using self supervised learning can improve model robustness and uncertainty"
    - "domain generalization by solving jigsaw puzzles"
    - "self supervised visual feature learning with deep neural networks a survey"
    - "unsupervised domain adaptation using feature whitening and consensus loss"
    - "learning image representations by completing damaged jigsaw puzzles"
    - "boosting self supervised learning via knowledge transfer"
    - "deep clustering for unsupervised learning of visual features"

Connected through hyperedge HE-8 (induced by source paper "diversify and match a domain adaptive representation learning paradigm for object detection"):
  with members:
    - "fully convolutional adaptation networks for semantic segmentation"
    - "learning to adapt structured output space for semantic segmentation"
    - "semantic foggy scene understanding with synthetic data"
    - "cross domain weakly supervised object detection through progressive domain adaptation"
    - "domain adaptive faster r cnn for object detection in the wild"
    - "no more discrimination cross city adaptation of road scene segmenters"
    - "cycada cycle consistent adversarial domain adaptation"
    - "focal loss for dense object detection"

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)