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: "artist agent a reinforcement learning approach to automatic stroke generation in oriental ink painting"
  Abstract: Oriental ink painting, called Sumi-e, is one of the most appealing painting styles that has attracted artists around the world. Major challenges in computer-based Sumi-e simulation are to abstract complex scene information and draw smooth and natural brush strokes. To automatically generate such strokes, we propose to model a brush as a reinforcement learning agent, and learn desired brush-trajectories by maximizing the sum of rewards in the policy search framework. We also elaborate on the design of actions, states, and rewards tailored for a Sumi-e agent. The effectiveness of our proposed approach is demonstrated through simulated Sumi-e experiments.

Connected through hyperedge HE-1 (induced by source paper "painting many pasts synthesizing time lapse videos of paintings"):
  with members:
    - "lpaintb learning to paint from self supervision"
    - "visual deprojection probabilistic recovery of collapsed dimensions"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "context aware synthesis for video frame interpolation"
    - "synthesizing programs for images using reinforced adversarial learning"
    - "learning to generate long term future via hierarchical prediction"
    - "super slomo high quality estimation of multiple intermediate frames for video interpolation"
    - "latent constraints learning to generate conditionally from unconditional generative models"

Connected through hyperedge HE-2 (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"
    - "neural painters a learned differentiable constraint for generating brushstroke paintings"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "learning to paint with model based deep reinforcement learning"):
  with members:
    - "learning to sketch with shortcut cycle consistency"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "investigating human priors for playing video games"
    - "learning to run challenge solutions adapting reinforcement learning methods for neuromusculoskeletal environments"
    - "recurrent world models facilitate policy evolution"
    - "sketch pix2seq a model to generate sketches of multiple categories"
    - "improved training of wasserstein gans"
    - "proximal policy optimization algorithms"

Connected through hyperedge HE-4 (induced by source paper "lpaintb learning to paint from self supervision"):
  with members:
    - "paintbot a reinforcement learning approach for natural media painting"
    - "revisiting self supervised visual representation learning"
    - "grasp2vec learning object representations from self supervised grasping"
    - "flow grounded spatial temporal video prediction from still images"
    - "an introduction to image synthesis with generative adversarial nets"
    - "unsupervised representation learning by predicting image rotations"
    - "hindsight experience replay"
    - "progressive growing of gans for improved quality stability and variation"

Connected through hyperedge HE-5 (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"
    - "neural painters a learned differentiable constraint for generating brushstroke paintings"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "paintbot a reinforcement learning approach for natural media painting"):
  with members:
    - "flow grounded spatial temporal video prediction from still images"
    - "an introduction to image synthesis with generative adversarial nets"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "universal style transfer via feature transforms"
    - "proximal policy optimization algorithms"
    - "a neural representation of sketch drawings"
    - "perceptual losses for real time style transfer and super resolution"
    - "scribbler controlling deep image synthesis with sketch and color"

Connected through hyperedge HE-7 (induced by source paper "neural painters a learned differentiable constraint for generating brushstroke paintings"):
  with members:
    - "learning latent dynamics for planning from pixels"
    - "deep learning for classical japanese literature"
    - "unsupervised image to sequence translation with canvas drawer networks"
    - "recurrent world models facilitate policy evolution"
    - "synthesizing programs for images using reinforced adversarial learning"
    - "can creative adversarial networks generating art by learning about styles and deviating from style norms"
    - "improved training of wasserstein gans"
    - "neural style transfer a review"

Connected through hyperedge HE-8 (induced by source paper "synthesizing programs for images using reinforced adversarial learning"):
  with members:
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "adversarial inverse graphics networks learning 2d to 3d lifting and image to image translation from unpaired supervision"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved training of wasserstein gans"
    - "csgnet neural shape parser for constructive solid geometry"
    - "learning human behaviors from motion capture by adversarial imitation"
    - "learning to infer graphics programs from hand drawn images"
    - "population based training of neural 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.LG(Machine Learning)