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: "configurable 3d scene synthesis and 2d image rendering with per pixel ground truth using stochastic grammars"
  Abstract: We propose a systematic learning-based approach to the generation of massive quantities of synthetic 3D scenes and arbitrary numbers of photorealistic 2D images thereof, with associated ground truth information, for the purposes of training, benchmarking, and diagnosing learning-based computer vision and robotics algorithms. In particular, we devise a learning-based pipeline of algorithms capable of automatically generating and rendering a potentially infinite variety of indoor scenes by using a stochastic grammar, represented as an attributed Spatial And-Or Graph, in conjunction with state-of-the-art physics-based rendering. Our pipeline is capable of synthesizing scene layouts with high diversity, and it is configurable inasmuch as it enables the precise customization and control of important attributes of the generated scenes. It renders photorealistic RGB images of the generated scenes while automatically synthesizing detailed, per-pixel ground truth data, including visible surface depth and normal, object identity, and material information (detailed to object parts), as well as environments (e.g., illuminations and camera viewpoints). We demonstrate the value of our synthesized dataset, by improving performance in certain machine-learning-based scene understanding tasks—depth and surface normal prediction, semantic segmentation, reconstruction, etc.—and by providing benchmarks for and diagnostics of trained models by modifying object attributes and scene properties in a controllable manner.

Connected through hyperedge HE-1 (induced by source paper "3d dynamic scene graphs actionable spatial perception with places objects and humans"):
  with members:
    - "teaser fast and certifiable point cloud registration"
    - "semantickitti a dataset for semantic scene understanding of lidar sequences"
    - "panoptic segmentation"
    - "in perfect shape certifiably optimal 3d shape reconstruction from 2d landmarks"
    - "semi supervised semantic mapping through label propagation with semantic texture meshes"
    - "kimera an open source library for real time metric semantic localization and mapping"
    - "3 d scene graph a sparse and semantic representation of physical environments for intelligent agents"
    - "value iteration networks on multiple levels of abstraction"

Connected through hyperedge HE-2 (induced by source paper "3d scene graph a structure for unified semantics 3d space and camera"):
  with members:
    - "efficient interactive annotation of segmentation datasets with polygon rnn"
    - "gibson env real world perception for embodied agents"
    - "a deep learning based behavioral approach to indoor autonomous navigation"
    - "learning human object interactions by graph parsing neural networks"
    - "fluid annotation a human machine collaboration interface for full image annotation"
    - "holistic 3d scene parsing and reconstruction from a single rgb image"
    - "blitznet a real time deep network for scene understanding"
    - "frustum pointnets for 3d object detection from rgb d data"

Connected through hyperedge HE-3 (induced by source paper "learning to generate synthetic 3d training data through hybrid gradient"):
  with members:
    - "unrealrox an extremely photorealistic virtual reality environment for robotics simulations and synthetic data generation"
    - "what makes good synthetic training data for learning disparity and optical flow estimation"
    - "cgintrinsics better intrinsic image decomposition through physically based rendering"
    - "gibson env real world perception for embodied agents"
    - "human centric indoor scene synthesis using stochastic grammar"
    - "building generalizable agents with a realistic and rich 3d environment"
    - "probabilistic model agnostic meta learning"
    - "domain randomization for transferring deep neural networks from simulation to the real world"

Connected through hyperedge HE-4 (induced by source paper "3d relnet joint object and relational network for 3d prediction"):
  with members:
    - "holistic 3d scene parsing and reconstruction from a single rgb image"
    - "cycada cycle consistent adversarial domain adaptation"
    - "a simple neural network module for relational reasoning"
    - "6 dof object pose from semantic keypoints"
    - "factoring shape pose and layout from the 2d image of a 3d scene"
    - "semantic scene completion from a single depth image"
    - "3d r2n2 a unified approach for single and multi view 3d object reconstruction"
    - "learning a predictable and generative vector representation for objects"

Connected through hyperedge HE-5 (induced by source paper "the robotrix an extremely photorealistic and very large scale indoor dataset of sequences with robot trajectories and interactions"):
  with members:
    - "unrealrox an extremely photorealistic virtual reality environment for robotics simulations and synthetic data generation"
    - "building generalizable agents with a realistic and rich 3d environment"
    - "sim4cv a photo realistic simulator for computer vision applications"
    - "minos multimodal indoor simulator for navigation in complex environments"
    - "home a household multimodal environment"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "looking beyond appearances synthetic training data for deep cnns in re identification"
    - "ai2 thor an interactive 3d environment for visual ai"

Connected through hyperedge HE-6 (induced by source paper "holistic scene understanding single view 3d holistic scene parsing and human pose estimation with human object interaction and physical commonsense"):
  with members:
    - "layoutnet reconstructing the 3d room layout from a single rgb image"
    - "human centric indoor scene synthesis using stochastic grammar"
    - "learning human object interactions by graph parsing neural networks"
    - "cooperative holistic scene understanding unifying 3d object layout and camera pose estimation"
    - "holistic 3d scene parsing and reconstruction from a single rgb image"
    - "learning pose grammar to encode human body configuration for 3d pose estimation"
    - "lifting from the deep convolutional 3d pose estimation from a single image"
    - "learning to detect human object interactions"

Connected through hyperedge HE-7 (induced by source paper "scenic a language for scenario specification and scene generation"):
  with members:
    - "verifai a toolkit for the design and analysis of artificial intelligence based systems"
    - "megapixel size image creation using generative adversarial networks"
    - "carla an open urban driving simulator"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "deepxplore automated whitebox testing of deep learning systems"
    - "recurrent topic transition gan for visual paragraph generation"
    - "driving in the matrix can virtual worlds replace human generated annotations for real world tasks"
    - "improved relation classification by deep recurrent neural networks with data augmentation"

Connected through hyperedge HE-8 (induced by source paper "vrgym a virtual testbed for physical and interactive ai"):
  with members:
    - "gibson env real world perception for embodied agents"
    - "human centric indoor scene synthesis using stochastic grammar"
    - "predicting human activities using stochastic grammar"
    - "learning social affordance grammar from videos transferring human interactions to human robot interactions"
    - "airsim high fidelity visual and physical simulation for autonomous vehicles"
    - "deepstack expert level artificial intelligence in heads up no limit poker"
    - "ai2 thor an interactive 3d environment for visual ai"
    - "semantic scene completion from a single depth image"

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)