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 cubist art"
  Abstract: Although the human visual system is surprisingly robust to extreme distortion when recognizing objects, most evaluations of computer object detection methods focus only on robustness to natural form deformations such as people's pose changes. To determine whether algorithms truly mirror the flexibility of human vision, they must be compared against human vision at its limits. For example, in Cubist abstract art, painted objects are distorted by object fragmentation and part-reorganization, to the point that human vision often fails to recognize them. In this paper, we evaluate existing object detection methods on these abstract renditions of objects, comparing human annotators to four state-of-the-art object detectors on a corpus of Picasso paintings. Our results demonstrate that while human perception significantly outperforms current methods, human perception and part-based models exhibit a similarly graceful degradation in object detection performance as the objects become increasingly abstract and fragmented, corroborating the theory of part-based object representation in the brain.

Connected through hyperedge HE-1 (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-2 (induced by source paper "satellite imagery multiscale rapid detection with windowed networks"):
  with members:
    - "xview objects in context in overhead imagery"
    - "you only look twice rapid multi scale object detection in satellite imagery"
    - "road extraction by deep residual u net"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "a large contextual dataset for classification detection and counting of cars with deep learning"
    - "a survey on object detection in optical remote sensing images"
    - "ssd single shot multibox detector"
    - "yolo9000 better faster stronger"

Connected through hyperedge HE-3 (induced by source paper "artistic domain generalisation methods are limited by their deep representations"):
  with members:
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "learning to generalize meta learning for domain generalization"
    - "bam the behance artistic media dataset for recognition beyond photography"
    - "deeper broader and artier domain generalization"
    - "generalisation and sharing in triplet convnets for sketch based visual search"
    - "deep neural networks with random gaussian weights a universal classification strategy"
    - "domain separation networks"
    - "domain generalization for object recognition with multi task autoencoders"

Connected through hyperedge HE-4 (induced by source paper "the imet collection 2019 challenge dataset"):
  with members:
    - "bam the behance artistic media dataset for recognition beyond photography"

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 "artistic object recognition by unsupervised style adaptation"):
  with members:
    - "cycada cycle consistent adversarial domain adaptation"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "adversarial discriminative domain adaptation"
    - "bam the behance artistic media dataset for recognition beyond photography"
    - "deeper broader and artier domain generalization"
    - "stylebank an explicit representation for neural image style transfer"
    - "coherent online video style transfer"
    - "perceptual losses for real time style transfer and super resolution"

Connected through hyperedge HE-7 (induced by source paper "identifying cross depicted historical motifs"):
  with members:
    - "deepdiva a highly functional python framework for reproducible experiments"
    - "robust physical world attacks on deep learning models"
    - "squeeze and excitation networks"
    - "deep residual learning for image recognition"
    - "the cross depiction problem computer vision algorithms for recognising objects in artwork and in photographs"
    - "learning to compare image patches via convolutional neural networks"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "deep neural networks are easily fooled high confidence predictions for unrecognizable images"

Connected through hyperedge HE-8 (induced by source paper "you only look twice rapid multi scale object detection in satellite imagery"):
  with members:
    - "deep residual learning for image recognition"
    - "the cross depiction problem computer vision algorithms for recognising objects in artwork and in photographs"

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)