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: "physically based rendering for indoor scene understanding using convolutional neural networks"
  Abstract: Indoor scene understanding is central to applications such as robot navigation and human companion assistance. Over the last years, data-driven deep neural networks have outperformed many traditional approaches thanks to their representation learning capabilities. One of the bottlenecks in training for better representations is the amount of available per-pixel ground truth data that is required for core scene understanding tasks such as semantic segmentation, normal prediction, and object edge detection. To address this problem, a number of works proposed using synthetic data. However, a systematic study of how such synthetic data is generated is missing. In this work, we introduce a large-scale synthetic dataset with 400K physically-based rendered images from 45K realistic 3D indoor scenes. We study the effects of rendering methods and scene lighting on training for three computer vision tasks: surface normal prediction, semantic segmentation, and object boundary detection. This study provides insights into the best practices for training with synthetic data (more realistic rendering is worth it) and shows that pretraining with our new synthetic dataset can improve results beyond the current state of the art on all three tasks.

Connected through hyperedge HE-1 (induced by source paper "domain decluttering simplifying images to mitigate synthetic real domain shift and improve depth estimation"):
  with members:
    - "modularized textual grounding for counterfactual resilience"
    - "geometry aware symmetric domain adaptation for monocular depth estimation"
    - "learning to adapt structured output space for semantic segmentation"
    - "dropblock a regularization method for convolutional networks"
    - "cgintrinsics better intrinsic image decomposition through physically based rendering"
    - "deep ordinal regression network for monocular depth estimation"
    - "taskonomy disentangling task transfer learning"
    - "t2net synthetic to realistic translation for solving single image depth estimation tasks"

Connected through hyperedge HE-2 (induced by source paper "block annotation better image annotation for semantic segmentation with sub image decomposition"):
  with members:
    - "efficient interactive annotation of segmentation datasets with polygon rnn"
    - "interactive full image segmentation by considering all regions jointly"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "learning to segment via cut and paste"
    - "cgintrinsics better intrinsic image decomposition through physically based rendering"
    - "learning pixel level semantic affinity with image level supervision for weakly supervised semantic segmentation"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "visual affordance and function understanding a survey"

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"
    - "training deep networks with synthetic data bridging the reality gap by domain randomization"
    - "pad net multi tasks guided prediction and distillation network for simultaneous depth estimation and scene parsing"

Connected through hyperedge HE-4 (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"
    - "training deep networks with synthetic data bridging the reality gap by domain randomization"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "um adapt unsupervised multi task adaptation using adversarial cross task distillation"):
  with members:
    - "learning to adapt structured output space for semantic segmentation"
    - "adadepth unsupervised content congruent adaptation for depth estimation"
    - "end to end multi task learning with attention"
    - "image to image translation for domain adaptation"
    - "analyzing modular cnn architectures for joint depth prediction and semantic segmentation"
    - "domain adaptation for visual applications a comprehensive survey"
    - "no more discrimination cross city adaptation of road scene segmenters"
    - "cycada cycle consistent adversarial domain adaptation"

Connected through hyperedge HE-6 (induced by source paper "adaptive composition gan towards realistic image synthesis"):
  with members:
    - "scene text synthesis for efficient and effective deep network training"
    - "few shot unsupervised image to image translation"
    - "semantic image synthesis with spatially adaptive normalization"
    - "st gan spatial transformer generative adversarial networks for image compositing"
    - "verisimilar image synthesis for accurate detection and recognition of texts in scenes"
    - "compositional gan learning conditional image composition"
    - "multimodal unsupervised image to image translation"
    - "gan dissection visualizing and understanding generative adversarial networks"

Connected through hyperedge HE-7 (induced by source paper "are we done with object recognition the icub robot s perspective"):
  with members:
    - "robotic tactile perception of object properties a review"
    - "learning hand eye coordination for robotic grasping with deep learning and large scale data collection"
    - "deep image retrieval learning global representations for image search"
    - "improved deep learning of object category using pose information"
    - "the curious robot learning visual representations via physical interactions"
    - "a deep representation for depth images from synthetic data"
    - "learning to recognize objects by retaining other factors of variation"
    - "scene recognition with cnns objects scales and dataset bias"

Connected through hyperedge HE-8 (induced by source paper "meta sim learning to generate synthetic datasets"):
  with members:
    - "learning to adapt structured output space for semantic segmentation"
    - "virtualhome simulating household activities via programs"
    - "semantic aware grad gan for virtual to real urban scene adaption"
    - "learning to simulate"
    - "multimodal unsupervised image to image translation"
    - "closing the sim to real loop adapting simulation randomization with real world experience"
    - "building generalizable agents with a realistic and rich 3d environment"
    - "large scale gan training for high fidelity natural image synthesis"

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)