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: "learning to paint with model based deep reinforcement learning"
  Abstract: We show how to teach machines to paint like human painters, who can use a small number of strokes to create fantastic paintings. By employing a neural renderer in model-based Deep Reinforcement Learning (DRL), our agents learn to determine the position and color of each stroke and make long-term plans to decompose texture-rich images into strokes. Experiments demonstrate that excellent visual effects can be achieved using hundreds of strokes. The training process does not require the experience of human painters or stroke tracking data. The code is available at this https URL.

Connected through hyperedge HE-1 (induced by source paper "unsupervised program synthesis for images using tree structured lstm"):
  with members:
    - "stochastic beams and where to find them the gumbel top k trick for sampling sequences without replacement"
    - "learning to infer and execute 3d shape programs"
    - "write execute assess program synthesis with a repl"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "3d aware scene manipulation via inverse graphics"
    - "a tree based decoder for neural machine translation"
    - "automatic program synthesis of long programs with a learned garbage collector"
    - "beyond pixel norm balls parametric adversaries using an analytically differentiable renderer"

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"
    - "neural painters a learned differentiable constraint for generating brushstroke paintings"
    - "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 "unsupervised doodling and painting with improved spiral"):
  with members:
    - "photo sketching inferring contour drawings from images"
    - "neural painters a learned differentiable constraint for generating brushstroke paintings"
    - "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-4 (induced by source paper "unsupervised minimax adversarial curiosity generative adversarial networks and predictability minimization"):
  with members:
    - "neural painters a learned differentiable constraint for generating brushstroke paintings"
    - "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-5 (induced by source paper "sketchtransfer a challenging new task for exploring detail invariance and the abstractions learned by deep networks"):
  with members:
    - "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"
    - "high frequency component helps explain the generalization of convolutional neural networks"

Connected through hyperedge HE-6 (induced by source paper "channel decomposition into painting actions"):
  with members:
    - "neural painters a learned differentiable constraint for generating brushstroke paintings"
    - "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-7 (induced by source paper "channel decomposition on generative networks"):
  with members:
    - "neural painters a learned differentiable constraint for generating brushstroke paintings"
    - "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)