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: "meta sim learning to generate synthetic datasets"
  Abstract: Training models to high-end performance requires availability of large labeled datasets, which are expensive to get. The goal of our work is to automatically synthesize labeled datasets that are relevant for a downstream task. We propose Meta-Sim, which learns a generative model of synthetic scenes, and obtain images as well as its corresponding ground-truth via a graphics engine. We parametrize our dataset generator with a neural network, which learns to modify attributes of scene graphs obtained from probabilistic scene grammars, so as to minimize the distribution gap between its rendered outputs and target data. If the real dataset comes with a small labeled validation set, we additionally aim to optimize a meta-objective, i.e. downstream task performance. Experiments show that the proposed method can greatly improve content generation quality over a human-engineered probabilistic scene grammar, both qualitatively and quantitatively as measured by performance on a downstream task.

Connected through hyperedge HE-1 (induced by source paper "stylegan2 distillation for feed forward image manipulation"):
  with members:
    - "style generator inversion for image enhancement and animation"
    - "image2stylegan how to embed images into the stylegan latent space"
    - "analyzing and improving the image quality of stylegan"
    - "learning continuous face age progression a pyramid of gans"
    - "face to parameter translation for game character auto creation"
    - "stargan v2 diverse image synthesis for multiple domains"
    - "classification accuracy score for conditional generative models"
    - "few shot unsupervised image to image translation"

Connected through hyperedge HE-2 (induced by source paper "neural data server a large scale search engine for transfer learning data"):
  with members:
    - "active domain randomization"
    - "an analysis of pre training on object detection"
    - "devil is in the edges learning semantic boundaries from noisy annotations"
    - "gated scnn gated shape cnns for semantic segmentation"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "learning to generate synthetic data via compositing"
    - "modanet a large scale street fashion dataset with polygon annotations"
    - "rethinking imagenet pre training"

Connected through hyperedge HE-3 (induced by source paper "learning to collide an adaptive safety critical scenarios generating method"):
  with members:
    - "generative adversarial minority oversampling"
    - "active domain randomization"
    - "adversarial autoaugment"
    - "deceptionnet network driven domain randomization"
    - "class balanced loss based on effective number of samples"
    - "fast autoaugment"
    - "cmts conditional multiple trajectory synthesizer for generating safety critical driving scenarios"
    - "vadra visual adversarial domain randomization and augmentation"

Connected through hyperedge HE-4 (induced by source paper "solving rubik s cube with a robot hand"):
  with members:
    - "sampling based planning of in hand manipulation with external pushes"
    - "active domain randomization"
    - "meta reinforcement learning with task embedding and shared policy"
    - "ai gas ai generating algorithms an alternate paradigm for producing general artificial intelligence"
    - "distilling policy distillation"
    - "deep dynamics models for learning dexterous manipulation"
    - "meta reinforcement learning as task inference"
    - "deceptionnet network driven domain randomization"

Connected through hyperedge HE-5 (induced by source paper "rsa randomized simulation as augmentation for robust human action recognition"):
  with members:
    - "ntu rgb d 120 a large scale benchmark for 3d human activity understanding"
    - "virtualhome simulating household activities via programs"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "structured domain randomization bridging the reality gap by context aware synthetic data"
    - "training deep networks with synthetic data bridging the reality gap by domain randomization"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "cycada cycle consistent adversarial domain adaptation"

Connected through hyperedge HE-6 (induced by source paper "identifying model weakness with adversarial examiner"):
  with members:
    - "using videos to evaluate image model robustness"
    - "embodied visual recognition"
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "multi granularity hierarchical attention fusion networks for reading comprehension and question answering"
    - "realistic adversarial examples in 3d meshes"
    - "deep nets what have they ever done for vision"
    - "strike with a pose neural networks are easily fooled by strange poses of familiar objects"
    - "black box adversarial attacks with limited queries and information"

Connected through hyperedge HE-7 (induced by source paper "representation learning through latent canonicalizations"):
  with members:
    - "are disentangled representations helpful for abstract visual reasoning"
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "a closed form solution to photorealistic image stylization"
    - "yolov3 an incremental improvement"
    - "semantic aware grad gan for virtual to real urban scene adaption"
    - "unsupervised representation learning by predicting image rotations"
    - "structured domain randomization bridging the reality gap by context aware synthetic data"

Connected through hyperedge HE-8 (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"
    - "neural painters a learned differentiable constraint for generating brushstroke paintings"
    - "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"

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)