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 and testing object detectors with virtual images"
  Abstract: In the area of computer vision, deep learning has produced a variety of state-of-the-art models that rely on massive labeled data. However, collecting and annotating images from the real world has a great demand for labor and money investments and is usually too passive to build datasets with specific characteristics, such as small area of objects and high occlusion level. Under the framework of Parallel Vision, this paper presents a purposeful way to design artificial scenes and automatically generate virtual images with precise annotations. A virtual dataset named ParallelEye is built, which can be used for several computer vision tasks. Then, by training the DPM (Deformable Parts Model) and Faster R-CNN detectors, we prove that the performance of models can be significantly improved by combining ParallelEye with publicly available real-world datasets during the training phase. In addition, we investigate the potential of testing the trained models from a specific aspect using intentionally designed virtual datasets, in order to discover the flaws of trained models. From the experimental results, we conclude that our virtual dataset is viable to train and test the object detectors.

Connected through hyperedge HE-1 (induced by source paper "adaptive and azimuth aware fusion network of multimodal local features for 3d object detection"):
  with members:
    - "stereo r cnn based 3d object detection for autonomous driving"
    - "pixor real time 3d object detection from point clouds"
    - "densefusion 6d object pose estimation by iterative dense fusion"
    - "ipod intensive point based object detector for point cloud"
    - "roarnet a robust 3d object detection based on region approximation refinement"
    - "pseudo lidar from visual depth estimation bridging the gap in 3d object detection for autonomous driving"
    - "cbam convolutional block attention module"
    - "orthographic feature transform for monocular 3d object detection"

Connected through hyperedge HE-2 (induced by source paper "weakly supervised object localization on grocery shelves using simple fcn and synthetic dataset"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "product recognition in store shelves as a sub graph isomorphism problem"
    - "deep self taught learning for weakly supervised object localization"
    - "synthesizing training data for object detection in indoor scenes"
    - "hide and seek forcing a network to be meticulous for weakly supervised object and action localization"
    - "deep learning logo detection with data expansion by synthesising context"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-3 (induced by source paper "a virtual testbed for critical incident investigation with autonomous remote aerial vehicle surveying artificial intelligence and decision support"):
  with members:
    - "a virtual environment with multi robot navigation analytics and decision support for critical incident investigation"
    - "using a game engine to simulate critical incidents and data collection by autonomous drones"
    - "virtual to real reinforcement learning for autonomous driving"
    - "dota a large scale dataset for object detection in aerial images"
    - "airsim high fidelity visual and physical simulation for autonomous vehicles"
    - "mask r cnn"

Connected through hyperedge HE-4 (induced by source paper "a virtual environment with multi robot navigation analytics and decision support for critical incident investigation"):
  with members:
    - "using a game engine to simulate critical incidents and data collection by autonomous drones"
    - "virtual to real reinforcement learning for autonomous driving"
    - "dota a large scale dataset for object detection in aerial images"
    - "airsim high fidelity visual and physical simulation for autonomous vehicles"
    - "faster r cnn towards real time object detection with region proposal networks"

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)