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: "weakly supervised object detection in artworks"
  Abstract: We propose a method for the weakly supervised detection of objects in paintings. At training time, only image-level annotations are needed. This, combined with the efficiency of our multiple-instance learning method, enables one to learn new classes on-the-fly from globally annotated databases, avoiding the tedious task of manually marking objects. We show on several databases that dropping the instance-level annotations only yields mild performance losses. We also introduce a new database, IconArt, on which we perform detection experiments on classes that could not be learned on photographs, such as Jesus Child or Saint Sebastian. To the best of our knowledge, these are the first experiments dealing with the automatic (and in our case weakly supervised) detection of iconographic elements in paintings. We believe that such a method is of great benefit for helping art historians to explore large digital databases.

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"
    - "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 "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"
    - "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"
    - "the cross depiction problem computer vision algorithms for recognising objects in artwork and in photographs"

Connected through hyperedge HE-3 (induced by source paper "discovering visual patterns in art collections with spatially consistent feature learning"):
  with members:
    - "can computers create art"
    - "neighbourhood consensus networks"
    - "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"
    - "detecting people in artwork with cnns"

Connected through hyperedge HE-4 (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"
    - "fine tuning cnn image retrieval with no human annotation"
    - "deep shape matching"
    - "rmpe regional multi person pose estimation"
    - "detecting people in artwork with cnns"
    - "you only look once unified real time object detection"
    - "large scale classification of fine art paintings learning the right metric on the right feature"

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)