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: "falling things a synthetic dataset for 3d object detection and pose estimation"
  Abstract: We present a new dataset, called Falling Things (FAT), for advancing the state-of-the-art in object detection and 3D pose estimation in the context of robotics. By synthetically combining object models and backgrounds of complex composition and high graphical quality, we are able to generate photorealistic images with accurate 3D pose annotations for all objects in all images. Our dataset contains 60k annotated photos of 21 household objects taken from the YCB dataset. For each image, we provide the 3D poses, per-pixel class segmentation, and 2D/3D bounding box coordinates for all objects. To facilitate testing different input modalities, we provide mono and stereo RGB images, along with registered dense depth images. We describe in detail the generation process and statistical analysis of the data.

Connected through hyperedge HE-1 (induced by source paper "synthtext3d synthesizing scene text images from 3d virtual worlds"):
  with members:
    - "progressive pose attention transfer for person image generation"
    - "an annotation saved is an annotation earned using fully synthetic training for object instance detection"
    - "arbitrary oriented scene text detection via rotation proposals"
    - "textsnake a flexible representation for detecting text of arbitrary shapes"
    - "pixellink detecting scene text via instance segmentation"
    - "verisimilar image synthesis for accurate detection and recognition of texts in scenes"
    - "effective use of synthetic data for urban scene semantic segmentation"
    - "textboxes a single shot oriented scene text detector"

Connected through hyperedge HE-2 (induced by source paper "pose estimation for texture less shiny objects in a single rgb image using synthetic training data"):
  with members:
    - "easylabel a semi automatic pixel wise object annotation tool for creating robotic rgb d datasets"
    - "densefusion 6d object pose estimation by iterative dense fusion"
    - "deep object pose estimation for semantic robotic grasping of household objects"
    - "training deep networks with synthetic data bridging the reality gap by domain randomization"
    - "sim4cv a photo realistic simulator for computer vision applications"
    - "t less an rgb d dataset for 6d pose estimation of texture less objects"
    - "real time seamless single shot 6d object pose prediction"
    - "labelfusion a pipeline for generating ground truth labels for real rgbd data of cluttered scenes"

Connected through hyperedge HE-3 (induced by source paper "training object detectors on synthetic images containing reflecting materials"):
  with members:
    - "deep object pose estimation for semantic robotic grasping of household objects"
    - "object detection using domain randomization and generative adversarial refinement of synthetic images"
    - "training deep networks with synthetic data bridging the reality gap by domain randomization"
    - "focal loss for dense object detection"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "on pre trained image features and synthetic images for deep learning"
    - "unrealcv connecting computer vision to unreal engine"
    - "unrealstereo controlling hazardous factors to analyze stereo vision"

Connected through hyperedge HE-4 (induced by source paper "deep object pose estimation for semantic robotic grasping of household objects"):
  with members:
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "discovery of latent 3d keypoints via end to end geometric reasoning"
    - "training deep networks with synthetic data bridging the reality gap by domain randomization"
    - "sim4cv a photo realistic simulator for computer vision applications"
    - "a self supervised learning system for object detection using physics simulation and multi view pose estimation"
    - "procedural modeling and physically based rendering for synthetic data generation in automotive applications"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "transferring end to end visuomotor control from simulation to real world for a multi stage task"

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)