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: "the cross depiction problem computer vision algorithms for recognising objects in artwork and in photographs"
  Abstract: The cross-depiction problem is that of recognising visual objects regardless of whether they are photographed, painted, drawn, etc. It is a potentially significant yet under-researched problem. Emulating the remarkable human ability to recognise objects in an astonishingly wide variety of depictive forms is likely to advance both the foundations and the applications of Computer Vision. #R##N#In this paper we benchmark classification, domain adaptation, and deep learning methods; demonstrating that none perform consistently well in the cross-depiction problem. Given the current interest in deep learning, the fact such methods exhibit the same behaviour as all but one other method: they show a significant fall in performance over inhomogeneous databases compared to their peak performance, which is always over data comprising photographs only. Rather, we find the methods that have strong models of spatial relations between parts tend to be more robust and therefore conclude that such information is important in modelling object classes regardless of appearance details.

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 "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-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"
    - "weakly supervised object detection in artworks"
    - "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"

Connected through hyperedge HE-5 (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"
    - "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"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-6 (induced by source paper "you only look twice rapid multi scale object detection in satellite imagery"):
  with members:
    - "deep residual learning for image recognition"
    - "detecting people in cubist art"

Connected through hyperedge HE-7 (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"
    - "going deeper with convolutions"
    - "toward automated discovery of artistic influence"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-8 (induced by source paper "object category understanding via eye fixations on freehand sketches"):
  with members:
    - "analyzing structural characteristics of object category representations from their semantic part distributions"
    - "deepfix a fully convolutional neural network for predicting human eye fixations"
    - "cat2000 a large scale fixation dataset for boosting saliency research"
    - "prediction of search targets from fixations in open world settings"

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)