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: "detecting people in artwork with cnns"
  Abstract: CNNs have massively improved performance in object detection in photographs. However research into object detection in artwork remains limited. We show state-of-the-art performance on a challenging dataset, People-Art, which contains people from photos, cartoons and 41 different artwork movements. We achieve this high performance by fine-tuning a CNN for this task, thus also demonstrating that training CNNs on photos results in overfitting for photos: only the first three or four layers transfer from photos to artwork. Although the CNN’s performance is the highest yet, it remains less than 60 % AP, suggesting further work is needed for the cross-depiction problem.

Connected through hyperedge HE-1 (induced by source paper "introduction to camera pose estimation with deep learning"):
  with members:
    - "learning metrics from teachers compact networks for image embedding"
    - "revealing scenes by inverting structure from motion reconstructions"
    - "understanding the limitations of cnn based absolute camera pose regression"
    - "learning loss for active learning"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "do better imagenet models transfer better"
    - "deep global relative networks for end to end 6 dof visual localization and odometry"
    - "deep auxiliary learning for visual localization and odometry"

Connected through hyperedge HE-2 (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"
    - "omniart multi task deep learning for artistic data analysis"

Connected through hyperedge HE-3 (induced by source paper "linking art through human poses"):
  with members:
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "how to read paintings semantic art understanding with multi modal retrieval"
    - "weakly supervised object detection in artworks"
    - "fine tuning cnn image retrieval with no human annotation"
    - "deep shape matching"
    - "rmpe regional multi person pose estimation"
    - "you only look once unified real time object detection"
    - "large scale classification of fine art paintings learning the right metric on the right feature"

Connected through hyperedge HE-4 (induced by source paper "cross domain weakly supervised object detection through progressive domain adaptation"):
  with members:
    - "focal loss for dense object detection"
    - "extreme clicking for efficient object annotation"
    - "adversarial discriminative domain adaptation"
    - "bam the behance artistic media dataset for recognition beyond photography"
    - "chainercv a library for deep learning in computer vision"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "multiple instance detection network with online instance classifier refinement"
    - "unsupervised pixel level domain adaptation with generative adversarial networks"

Connected through hyperedge HE-5 (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"

Connected through hyperedge HE-6 (induced by source paper "savoias a diverse multi category visual complexity dataset"):
  with members:
    - "automatic understanding of image and video advertisements"
    - "microsoft coco common objects in context"
    - "rank aggregation via nuclear norm minimization"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-7 (induced by source paper "omniart multi task deep learning for artistic data analysis"):
  with members:
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "convolutional sketch inversion"
    - "ubernet training a universal convolutional neural network for low mid and high level vision using diverse datasets and limited memory"
    - "cross stitch networks for multi task learning"
    - "multi task zero shot action recognition with prioritised data augmentation"
    - "deep residual learning for image recognition"
    - "large scale classification of fine art paintings learning the right metric on the right feature"
    - "quantifying creativity in art networks"

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)