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 shape of art history in the eyes of the machine"
  Abstract: How does the machine classify styles in art? And how does it relate to art historians's methods for analyzing style? Several studies have shown the ability of the machine to learn and predict style categories, such as Renaissance, Baroque, Impressionism, etc., from images of paintings. This implies that the machine can learn an internal representation encoding discriminative features through its visual analysis. However, such a representation is not necessarily interpretable. We conducted a comprehensive study of several of the state-of-the-art convolutional neural networks applied to the task of style classification on 77K images of paintings, and analyzed the learned representation through correlation analysis with concepts derived from art history. Surprisingly, the networks could place the works of art in a smooth temporal arrangement mainly based on learning style labels, without any a priori knowledge of time of creation, the historical time and context of styles, or relations between styles. The learned representations showed that there are few underlying factors that explain the visual variations of style in art. Some of these factors were found to correlate with style patterns suggested by Heinrich W\"olfflin (1846-1945). The learned representations also consistently highlighted certain artists as the extreme distinctive representative of their styles, which quantitatively confirms art historian observations.

Connected through hyperedge HE-1 (induced by source paper "sketch to art synthesizing stylized art images from sketches"):
  with members:
    - "semantic image synthesis with spatially adaptive normalization"
    - "cgans with projection discriminator"
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "multimodal unsupervised image to image translation"
    - "sketchygan towards diverse and realistic sketch to image synthesis"
    - "a style based generator architecture for generative adversarial networks"
    - "style and content disentanglement in generative adversarial networks"
    - "drit diverse image to image translation via disentangled representations"

Connected through hyperedge HE-2 (induced by source paper "oogan disentangling gan with one hot sampling and orthogonal regularization"):
  with members:
    - "learning feature to feature translator by alternating back propagation for zero shot learning"
    - "infogan cr disentangling generative adversarial networks with contrastive regularizers"
    - "entropy and mutual information in models of deep neural networks"
    - "semantic guided multi attention localization for zero shot learning"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "spectral normalization for generative adversarial networks"
    - "can we gain more from orthogonality regularizations in training deep cnns"
    - "isolating sources of disentanglement in variational autoencoders"

Connected through hyperedge HE-3 (induced by source paper "historical and modern features for buddha statue classification"):
  with members:
    - "context aware embeddings for automatic art analysis"
    - "how to read paintings semantic art understanding with multi modal retrieval"
    - "joint 3d face reconstruction and dense alignment with position map regression network"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "omniart multi task deep learning for artistic data analysis"
    - "node2vec scalable feature learning for networks"
    - "deep residual learning for image recognition"
    - "large scale classification of fine art paintings learning the right metric on the right feature"

Connected through hyperedge HE-4 (induced by source paper "machine the new art connoisseur"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "deep residual learning for image recognition"
    - "rethinking the inception architecture for computer vision"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-5 (induced by source paper "deep learning architect classification for architectural design through the eye of artificial intelligence"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "treepedia 2 0 applying deep learning for large scale quantification of urban tree cover"
    - "learning transferable architectures for scalable image recognition"
    - "deep residual learning for image recognition"
    - "toward automated discovery of artistic influence"
    - "very deep convolutional networks for large scale image recognition"
    - "neural machine translation by jointly learning to align and translate"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

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.AI(Artificial Intelligence)