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: "training deep networks with synthetic data bridging the reality gap by domain randomization"
  Abstract: We present a system for training deep neural networks for object detection using synthetic images. To handle the variability in real-world data, the system relies upon the technique of domain randomization, in which the parameters of the simulator$-$such as lighting, pose, object textures, etc.$-$are randomized in non-realistic ways to force the neural network to learn the essential features of the object of interest. We explore the importance of these parameters, showing that it is possible to produce a network with compelling performance using only non-artistically-generated synthetic data. With additional fine-tuning on real data, the network yields better performance than using real data alone. This result opens up the possibility of using inexpensive synthetic data for training neural networks while avoiding the need to collect large amounts of hand-annotated real-world data or to generate high-fidelity synthetic worlds$-$both of which remain bottlenecks for many applications. The approach is evaluated on bounding box detection of cars on the KITTI dataset.

Connected through hyperedge HE-1 (induced by source paper "increasing generality in machine learning through procedural content generation"):
  with members:
    - "fully differentiable procedural content generation through generative playing networks"
    - "pcgrl procedural content generation via reinforcement learning"
    - "ai gas ai generating algorithms an alternate paradigm for producing general artificial intelligence"
    - "leveraging procedural generation to benchmark reinforcement learning"
    - "procedural content generation through quality diversity"
    - "arena a general evaluation platform and building toolkit for multi agent intelligence"
    - "continual lifelong learning with neural networks a review"
    - "deceptionnet network driven domain randomization"

Connected through hyperedge HE-2 (induced by source paper "template based unseen instance detection"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "deceptionnet network driven domain randomization"
    - "learning to track any object"
    - "on the convergence of adam and beyond"
    - "photorealistic image synthesis for object instance detection"
    - "target aware deep tracking"
    - "deep model based 6d pose refinement in rgb"
    - "bop benchmark for 6d object pose estimation"

Connected through hyperedge HE-3 (induced by source paper "task assisted domain adaptation with anchor tasks"):
  with members:
    - "domain adaptation for structured output via discriminative patch representations"
    - "learning to adapt structured output space for semantic segmentation"
    - "cross domain weakly supervised object detection through progressive domain adaptation"
    - "taskonomy disentangling task transfer learning"
    - "3d human pose estimation in the wild by adversarial learning"
    - "regularizing deep networks by modeling and predicting label structure"
    - "pad net multi tasks guided prediction and distillation network for simultaneous depth estimation and scene parsing"
    - "cycada cycle consistent adversarial domain adaptation"

Connected through hyperedge HE-4 (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"
    - "meta sim learning to generate synthetic datasets"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "learning to generate synthetic data via compositing"
    - "modanet a large scale street fashion dataset with polygon annotations"

Connected through hyperedge HE-5 (induced by source paper "sviro synthetic vehicle interior rear seat occupancy dataset and benchmark"):
  with members:
    - "how much real data do we actually need analyzing object detection performance using synthetic and real data"
    - "eurocity persons a novel benchmark for person detection in traffic scenes"
    - "monet unsupervised scene decomposition and representation"
    - "moment matching for multi source domain adaptation"
    - "syn2real a new benchmark forsynthetic to real visual domain adaptation"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "museum exhibit identification challenge for domain adaptation and beyond"

Connected through hyperedge HE-6 (induced by source paper "learning object placements for relational instructions by hallucinating scene representations"):
  with members:
    - "self supervised 3d shape and viewpoint estimation from single images for robotics"
    - "fast scnn fast semantic segmentation network"
    - "interactive visual grounding of referring expressions for human robot interaction"
    - "referring relationships"
    - "st gan spatial transformer generative adversarial networks for image compositing"
    - "context aware synthesis and placement of object instances"
    - "mattnet modular attention network for referring expression comprehension"
    - "a multimodal classifier generative adversarial network for carry and place tasks from ambiguous language instructions"

Connected through hyperedge HE-7 (induced by source paper "the benefits of synthetic data for action categorization"):
  with members:
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "compressed video action recognition"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "cut paste and learn surprisingly easy synthesis for instance detection"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "lattice long short term memory for human action recognition"
    - "the kinetics human action video dataset"
    - "learning from synthetic data addressing domain shift for semantic segmentation"

Connected through hyperedge HE-8 (induced by source paper "the synthinel 1 dataset a collection of high resolution synthetic overhead imagery for building segmentation"):
  with members:
    - "deepglobe 2018 a challenge to parse the earth through satellite images"
    - "ternausnetv2 fully convolutional network for instance segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "dota a large scale dataset for object detection in aerial images"
    - "learning from synthetic data addressing domain shift for semantic segmentation"
    - "satellite imagery feature detection using deep convolutional neural network a kaggle competition"
    - "rethinking atrous convolution for semantic image segmentation"

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)