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: "neural painters a learned differentiable constraint for generating brushstroke paintings"
  Abstract: We explore neural painters, a generative model for brushstrokes learned from a real non-differentiable and non-deterministic painting program. We show that when training an agent to "paint" images using brushstrokes, using a differentiable neural painter leads to much faster convergence. We propose a method for encouraging this agent to follow human-like strokes when reconstructing digits. We also explore the use of a neural painter as a differentiable image parameterization. By directly optimizing brushstrokes to activate neurons in a pre-trained convolutional network, we can directly visualize ImageNet categories and generate "ideal" paintings of each class. Finally, we present a new concept called intrinsic style transfer. By minimizing only the content loss from neural style transfer, we allow the artistic medium, in this case, brushstrokes, to naturally dictate the resulting style.

Connected through hyperedge HE-1 (induced by source paper "interactive neural style transfer with artists"):
  with members:
    - "a content transformation block for image style transfer"
    - "style transfer by relaxed optimal transport and self similarity"
    - "dialog on a canvas with a machine"
    - "wasserstein style transfer"
    - "multimodal style transfer via graph cuts"
    - "a style aware content loss for real time hd style transfer"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "synthesizing programs for images using reinforced adversarial learning"

Connected through hyperedge HE-2 (induced by source paper "kaokore a pre modern japanese art facial expression dataset"):
  with members:
    - "tailorgan making user defined fashion designs"
    - "learning to paint with model based deep reinforcement learning"
    - "kuronet pre modern japanese kuzushiji character recognition with deep learning"
    - "pytorch an imperative style high performance deep learning library"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "deep learning for classical japanese literature"
    - "full body high resolution anime generation with progressive structure conditional generative adversarial networks"
    - "synthesizing programs for images using reinforced adversarial learning"

Connected through hyperedge HE-3 (induced by source paper "why do line drawings work a realism hypothesis"):
  with members:
    - "towards robust monocular depth estimation mixing datasets for zero shot cross dataset transfer"
    - "unsupervised doodling and painting with improved spiral"

Connected through hyperedge HE-4 (induced by source paper "unsupervised doodling and painting with improved spiral"):
  with members:
    - "photo sketching inferring contour drawings from images"
    - "learning to paint with model based deep reinforcement learning"
    - "meta sim learning to generate synthetic datasets"
    - "pragmatic inference and visual abstraction enable contextual flexibility during visual communication"
    - "generating diverse programs with instruction conditioned reinforced adversarial learning"
    - "can computers create art"
    - "learning to search with mctsnets"
    - "learning to simulate"

Connected through hyperedge HE-5 (induced by source paper "unsupervised minimax adversarial curiosity generative adversarial networks and predictability minimization"):
  with members:
    - "learning to paint with model based deep reinforcement learning"
    - "exploration by random network distillation"
    - "large scale study of curiosity driven learning"
    - "world models"
    - "one big net for everything"
    - "synthesizing programs for images using reinforced adversarial learning"
    - "coulomb gans provably optimal nash equilibria via potential fields"
    - "curiosity driven exploration by self supervised prediction"

Connected through hyperedge HE-6 (induced by source paper "sketchtransfer a challenging new task for exploring detail invariance and the abstractions learned by deep networks"):
  with members:
    - "learning to paint with model based deep reinforcement learning"
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "kuronet pre modern japanese kuzushiji character recognition with deep learning"
    - "recurrent independent mechanisms"
    - "virtual mixup training for unsupervised domain adaptation"
    - "graphmix regularized training of graph neural networks for semi supervised learning"
    - "mixmatch a holistic approach to semi supervised learning"
    - "high frequency component helps explain the generalization of convolutional neural networks"

Connected through hyperedge HE-7 (induced by source paper "channel decomposition into painting actions"):
  with members:
    - "learning to paint with model based deep reinforcement learning"
    - "learning latent dynamics for planning from pixels"
    - "image to image translation via group wise deep whitening and coloring transformation"
    - "large scale gan training for high fidelity natural image synthesis"
    - "a style based generator architecture for generative adversarial networks"
    - "recurrent world models facilitate policy evolution"
    - "synthesizing programs for images using reinforced adversarial learning"
    - "multi style generative network for real time transfer"

Connected through hyperedge HE-8 (induced by source paper "channel decomposition on generative networks"):
  with members:
    - "learning to paint with model based deep reinforcement learning"
    - "learning latent dynamics for planning from pixels"
    - "image to image translation via group wise deep whitening and coloring transformation"
    - "a style based generator architecture for generative adversarial networks"
    - "recurrent world models facilitate policy evolution"
    - "multi style generative network for real time transfer"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "universal style transfer via feature transforms"

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)