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: "seeing behind the camera identifying the authorship of a photograph"
  Abstract: We introduce the novel problem of identifying the photographer behind a photograph. To explore the feasibility of current computer vision techniques to address this problem, we created a new dataset of over 180,000 images taken by 41 well-known photographers. Using this dataset, we examined the effectiveness of a variety of features (low and high-level, including CNN features) at identifying the photographer. We also trained a new deep convolutional neural network for this task. Our results show that high-level features greatly outperform low-level features. We provide qualitative results using these learned models that give insight into our method's ability to distinguish between photographers, and allow us to draw interesting conclusions about what specific photographers shoot. We also demonstrate two applications of our method.

Connected through hyperedge HE-1 (induced by source paper "a report on personally identifiable sensor data from smartphone devices"):
  with members:
    - "predicting floor level for 911 calls with neural networks and smartphone sensor data"
    - "mobile phone identification through the built in magnetometers"
    - "planet photo geolocation with convolutional neural networks"
    - "hmog new behavioral biometric features for continuous authentication of smartphone users"
    - "data driven authentication on the effectiveness of user behaviour modelling with mobile device sensors"
    - "pin skimming exploiting the ambient light sensor in mobile devices"
    - "mobile device identification via sensor fingerprinting"
    - "an egocentric look at video photographer identity"

Connected through hyperedge HE-2 (induced by source paper "distance preserving grid layouts"):
  with members:
    - "the state of the art in cartograms"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-3 (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-4 (induced by source paper "automatic understanding of image and video advertisements"):
  with members:
    - "end to end saliency mapping via probability distribution prediction"
    - "deep reconstruction classification networks for unsupervised domain adaptation"
    - "walk and learn facial attribute representation learning from egocentric video and contextual data"
    - "what happens if learning to predict the effect of forces in images"
    - "learning aligned cross modal representations from weakly aligned data"
    - "domain separation networks"
    - "unsupervised domain adaptation with residual transfer networks"
    - "whittlesearch interactive image search with relative attribute feedback"

Connected through hyperedge HE-5 (induced by source paper "can we teach computers to understand art domain adaptation for enhancing deep networks capacity to de abstract art"):
  with members:
    - "demystifying neural style transfer"
    - "negative results in computer vision a perspective"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "perceptual losses for real time style transfer and super resolution"
    - "texture networks feed forward synthesis of textures and stylized images"
    - "fast patch based style transfer of arbitrary style"
    - "scene recognition with cnns objects scales and dataset bias"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-6 (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"
    - "understanding neural networks through deep visualization"
    - "a neural algorithm of artistic style"
    - "the cross depiction problem computer vision algorithms for recognising objects in artwork and in photographs"
    - "going deeper with convolutions"
    - "toward automated discovery of artistic influence"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-7 (induced by source paper "could you guess an interesting movie from the posters an evaluation of vision based features on movie poster database"):
  with members:
    - "very deep convolutional networks for large scale image recognition"
    - "recognizing image style"

Connected through hyperedge HE-8 (induced by source paper "what is around the camera"):
  with members:
    - "radiometric scene decomposition scene reflectance illumination and geometry from rgb d images"
    - "deep outdoor illumination estimation"
    - "novel views of objects from a single image"
    - "delight net decomposing reflectance maps into specular materials and natural illumination"
    - "deep reflectance maps"
    - "direct intrinsics learning albedo shading decomposition by convolutional regression"
    - "constrained structured regression with convolutional neural networks"
    - "designing deep networks for surface normal estimation"

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)