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: "high performance and tunable stereo reconstruction"
  Abstract: Traditional stereo algorithms have focused their efforts on reconstruction quality and have largely avoided prioritizing for run time performance. Robots, on the other hand, require quick maneuverability and effective computation to observe its immediate environment and perform tasks within it. In this work, we propose a high-performance and tunable stereo disparity estimation method, with a peak frame-rate of 120Hz (VGA resolution, on a single CPU-thread), that can potentially enable robots to quickly reconstruct their immediate surroundings and maneuver at high-speeds. Our key contribution is a disparity estimation algorithm that iteratively approximates the scene depth via a piece-wise planar mesh from stereo imagery, with a fast depth validation step for semi-dense reconstruction. The mesh is initially seeded with sparsely matched keypoints, and is recursively tessellated and refined as needed (via a resampling stage), to provide the desired stereo disparity accuracy. The inherent simplicity and speed of our approach, with the ability to tune it to a desired reconstruction quality and runtime performance makes it a compelling solution for applications in high-speed vehicles.

Connected through hyperedge HE-1 (induced by source paper "incremental visual inertial 3d mesh generation with structural regularities"):
  with members:
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "on manifold preintegration for real time visual inertial odometry"

Connected through hyperedge HE-2 (induced by source paper "real time dense mapping for self driving vehicles using fisheye cameras"):
  with members:
    - "project autovision localization and 3d scene perception for an autonomous vehicle with a multi camera system"
    - "efficient 2d 3d matching for multi camera visual localization"
    - "yolov3 an incremental improvement"
    - "robust dense mapping for large scale dynamic environments"
    - "toward driving scene understanding a dataset for learning driver behavior and causal reasoning"
    - "slanted stixels representing san francisco s steepest streets"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "instance aware semantic segmentation via multi task network cascades"

Connected through hyperedge HE-3 (induced by source paper "sparse depth sensing for resource constrained robots"):
  with members:
    - "deep depth completion of a single rgb d image"
    - "compressed sensing for scalable robotic tactile skins"
    - "deeper depth prediction with fully convolutional residual networks"
    - "adaptive foveated single pixel imaging with dynamic super sampling"
    - "orb slam a versatile and accurate monocular slam system"
    - "depth reconstruction from sparse samples representation algorithm and sampling"
    - "stable image reconstruction using total variation minimization"
    - "robust sparse analysis regularization"

Connected through hyperedge HE-4 (induced by source paper "fast robust monocular depth estimation for obstacle detection with fully convolutional networks"):
  with members:
    - "segnet a deep convolutional encoder decoder architecture for robust semantic pixel wise labelling"
    - "flownet learning optical flow with convolutional networks"
    - "learning deconvolution network for semantic segmentation"
    - "very deep convolutional networks for large scale image recognition"
    - "depth map prediction from a single image using a multi scale deep network"
    - "vision and learning for deliberative monocular cluttered flight"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-5 (induced by source paper "attention and anticipation in fast visual inertial navigation"):
  with members:
    - "past present and future of simultaneous localization and mapping toward the robust perception age"
    - "on manifold preintegration theory for fast and accurate visual inertial navigation"
    - "you only look once unified real time object detection"
    - "probe predictive robust estimation for visual inertial navigation"
    - "orb slam a versatile and accurate monocular slam system"
    - "recurrent models of visual attention"

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.RO(Robotics)