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: "artgan artwork synthesis with conditional categorical gans"
  Abstract: This paper proposes an extension to the Generative Adversarial Networks (GANs), namely as ARTGAN to synthetically generate more challenging and complex images such as artwork that have abstract characteristics. This is in contrast to most of the current solutions that focused on generating natural images such as room interiors, birds, flowers and faces. The key innovation of our work is to allow back-propagation of the loss function w.r.t. the labels (randomly assigned to each generated images) to the generator from the discriminator. With the feedback from the label information, the generator is able to learn faster and achieve better generated image quality. Empirically, we show that the proposed ARTGAN is capable to create realistic artwork, as well as generate compelling real world images that globally look natural with clear shape on CIFAR-10.

Connected through hyperedge HE-1 (induced by source paper "simple and effective prevention of mode collapse in deep one class classification"):
  with members:
    - "deep semi supervised anomaly detection"
    - "robust anomaly detection in images using adversarial autoencoders"
    - "efficient gan based anomaly detection"
    - "deep anomaly detection using geometric transformations"
    - "escaping from collapsing modes in a constrained space"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "began boundary equilibrium generative adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "realistic river image synthesis using deep generative adversarial networks"):
  with members:
    - "decentralized flood forecasting using deep neural networks"
    - "how good is my gan"
    - "a style based generator architecture for generative adversarial networks"
    - "generalized cross entropy loss for training deep neural networks with noisy labels"
    - "generative modeling using the sliced wasserstein distance"
    - "progressive growing of gans for improved quality stability and variation"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "adversarial generation of training examples applications to moving vehicle license plate recognition"

Connected through hyperedge HE-3 (induced by source paper "toward a neuro inspired creative decoder"):
  with members:
    - "novelty detection with gan"
    - "inference suboptimality in variational autoencoders"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "can creative adversarial networks generating art by learning about styles and deviating from style norms"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "infovae information maximizing variational autoencoders"
    - "a neural representation of sketch drawings"
    - "generative adversarial text to image synthesis"

Connected through hyperedge HE-4 (induced by source paper "systematic analysis of image generation using gans"):
  with members:
    - "sketchygan towards diverse and realistic sketch to image synthesis"
    - "midinet a convolutional generative adversarial network for symbolic domain music generation"
    - "texturegan controlling deep image synthesis with texture patches"
    - "semantic image synthesis via adversarial learning"
    - "face aging with conditional generative adversarial networks"
    - "attngan fine grained text to image generation with attentional generative adversarial networks"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "stackgan realistic image synthesis with stacked generative adversarial networks"

Connected through hyperedge HE-5 (induced by source paper "accelerating deconvolution on unmodified cnn accelerators for generative adversarial networks a software approach"):
  with members:
    - "towards a uniform architecture for the efficient implementation of 2d and 3d deconvolutional neural networks on fpgas"
    - "ganax a unified mimd simd acceleration for generative adversarial networks"
    - "spectral normalization for generative adversarial networks"
    - "triple generative adversarial nets"
    - "perceptual generative adversarial networks for small object detection"
    - "gp gan towards realistic high resolution image blending"
    - "a design methodology for efficient implementation of deconvolutional neural networks on an fpga"
    - "automatic vertebra labeling in large scale 3d ct using deep image to image network with message passing and sparsity regularization"

Connected through hyperedge HE-6 (induced by source paper "dialog on a canvas with a machine"):
  with members:
    - "copy the old or paint anew an adversarial framework for non parametric image stylization"
    - "can creative adversarial networks generating art by learning about styles and deviating from style norms"
    - "towards the automatic anime characters creation with generative adversarial networks"
    - "a neural representation of sketch drawings"
    - "generating sequences with recurrent neural networks"

Connected through hyperedge HE-7 (induced by source paper "autonomy authenticity authorship and intention in computer generated art"):
  with members:
    - "can creative adversarial networks generating art by learning about styles and deviating from style norms"
    - "multi style generative network for real time transfer"
    - "tweeting ai perceptions of lay vs expert twitterati"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"

Connected through hyperedge HE-8 (induced by source paper "adversarial training in affective computing and sentiment analysis recent advances and perspectives"):
  with members:
    - "geometry contrastive gan for facial expression transfer"
    - "generative adversarial networks an overview"
    - "towards open set identity preserving face synthesis"
    - "talking face generation by conditional recurrent adversarial network"
    - "generative adversarial talking head bringing portraits to life with a weakly supervised neural network"
    - "interactive generative adversarial networks for facial expression generation in dyadic interactions"
    - "expressive speech synthesis via modeling expressions with variational autoencoder"
    - "voice impersonation using generative adversarial networks"

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)