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: "synthesizing programs for images using reinforced adversarial learning"
  Abstract: Advances in deep generative networks have led to impressive results in recent years. Nevertheless, such models can often waste their capacity on the minutiae of datasets, presumably due to weak inductive biases in their decoders. This is where graphics engines may come in handy since they abstract away low-level details and represent images as high-level programs. Current methods that combine deep learning and renderers are limited by hand-crafted likelihood or distance functions, a need for large amounts of supervision, or difficulties in scaling their inference algorithms to richer datasets. To mitigate these issues, we present SPIRAL, an adversarially trained agent that generates a program which is executed by a graphics engine to interpret and sample images. The goal of this agent is to fool a discriminator network that distinguishes between real and rendered data, trained with a distributed reinforcement learning setup without any supervision. A surprising finding is that using the discriminator's output as a reward signal is the key to allow the agent to make meaningful progress at matching the desired output rendering. To the best of our knowledge, this is the first demonstration of an end-to-end, unsupervised and adversarial inverse graphics agent on challenging real world (MNIST, Omniglot, CelebA) and synthetic 3D datasets.

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"
    - "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"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-2 (induced by source paper "unsupervised program synthesis for images using tree structured lstm"):
  with members:
    - "learning to paint with model based deep reinforcement learning"
    - "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"

Connected through hyperedge HE-3 (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-4 (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"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "object discovery with a copy pasting gan"):
  with members:
    - "tf replicator distributed machine learning for researchers"
    - "inserting videos into videos"
    - "monet unsupervised scene decomposition and representation"
    - "unsupervised moving object detection via contextual information separation"
    - "multi object representation learning with iterative variational inference"
    - "learning to generate synthetic data via compositing"
    - "unsupervised image matching and object discovery as optimization"
    - "learning to segment via cut and paste"

Connected through hyperedge HE-6 (induced by source paper "interactive sketch fill multiclass sketch to image translation"):
  with members:
    - "im2pencil controllable pencil illustration from photographs"
    - "semantic image synthesis with spatially adaptive normalization"
    - "faceshop deep sketch based face image editing"
    - "multimodal unsupervised image to image translation"
    - "spectral normalization for generative adversarial networks"
    - "sketchygan towards diverse and realistic sketch to image synthesis"
    - "a style based generator architecture for generative adversarial networks"
    - "which training methods for gans do actually converge"

Connected through hyperedge HE-7 (induced by source paper "exploiting hierarchy for learning and transfer in kl regularized rl"):
  with members:
    - "information asymmetry in kl regularized rl"
    - "learning to share and hide intentions using information regularization"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "near optimal representation learning for hierarchical reinforcement learning"
    - "learning dexterous in hand manipulation"
    - "dexterous manipulation with deep reinforcement learning efficient general and low cost"
    - "latent space policies for hierarchical reinforcement learning"
    - "diversity is all you need learning skills without a reward function"

Connected through hyperedge HE-8 (induced by source paper "relational mimic for visual adversarial imitation learning"):
  with members:
    - "tactilegcn a graph convolutional network for predicting grasp stability with tactile sensors"
    - "stand alone self attention in vision models"
    - "the gan landscape losses architectures regularization and normalization"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "learning dexterous in hand manipulation"
    - "two stream adaptive graph convolutional networks for skeleton based action recognition"
    - "generative adversarial imitation from observation"
    - "spectral normalization for 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)