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: "quantifying creativity in art networks"
  Abstract: Can we develop a computer algorithm that assesses the creativity of a painting given its context within art history? This paper proposes a novel computational framework for assessing the creativity of creative products, such as paintings, sculptures, poetry, etc. We use the most common definition of creativity, which emphasizes the originality of the product and its influential value. The proposed computational framework is based on constructing a network between creative products and using this network to infer about the originality and influence of its nodes. Through a series of transformations, we construct a Creativity Implication Network. We show that inference about creativity in this network reduces to a variant of network centrality problems which can be solved efficiently. We apply the proposed framework to the task of quantifying creativity of paintings (and sculptures). We experimented on two datasets with over 62K paintings to illustrate the behavior of the proposed framework. We also propose a methodology for quantitatively validating the results of the proposed algorithm, which we call the "time machine experiment".

Connected through hyperedge HE-1 (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"
    - "the shape of art history in the eyes of the machine"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "and now for something completely different visual novelty in an online network of designers"):
  with members:
    - "bam the behance artistic media dataset for recognition beyond photography"
    - "why do men get more attention exploring factors behind success in an online design community"
    - "an image is worth more than a thousand favorites surfacing the hidden beauty of flickr pictures"
    - "going deeper with convolutions"
    - "6 seconds of sound and vision creativity in micro videos"
    - "an extended cencov campbell characterization of conditional information geometry"

Connected through hyperedge HE-3 (induced by source paper "omniart multi task deep learning for artistic data analysis"):
  with members:
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "convolutional sketch inversion"
    - "ubernet training a universal convolutional neural network for low mid and high level vision using diverse datasets and limited memory"
    - "detecting people in artwork with cnns"
    - "cross stitch networks for multi task learning"
    - "multi task zero shot action recognition with prioritised data augmentation"
    - "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 "can creative adversarial networks generating art by learning about styles and deviating from style norms"):
  with members:
    - "perceptual losses for real time style transfer and super resolution"
    - "generative adversarial text to image synthesis"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "empirical evaluation of rectified activations in convolutional network"
    - "incorporating characteristics of human creativity into an evolutionary art algorithm"

Connected through hyperedge HE-5 (induced by source paper "high level concepts for affective understanding of images"):
  with members:
    - "robust image sentiment analysis using progressively trained and domain transferred deep networks"
    - "what value do explicit high level concepts have in vision to language problems"
    - "deep residual learning for image recognition"

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)