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: "a photometrically calibrated benchmark for monocular visual odometry"
  Abstract: We present a dataset for evaluating the tracking accuracy of monocular visual odometry and SLAM methods. It contains 50 real-world sequences comprising more than 100 minutes of video, recorded across dozens of different environments -- ranging from narrow indoor corridors to wide outdoor scenes. All sequences contain mostly exploring camera motion, starting and ending at the same position. This allows to evaluate tracking accuracy via the accumulated drift from start to end, without requiring ground truth for the full sequence. In contrast to existing datasets, all sequences are photometrically calibrated. We provide exposure times for each frame as reported by the sensor, the camera response function, and dense lens attenuation factors. We also propose a novel, simple approach to non-parametric vignette calibration, which requires minimal set-up and is easy to reproduce. Finally, we thoroughly evaluate two existing methods (ORB-SLAM and DSO) on the dataset, including an analysis of the effect of image resolution, camera field of view, and the camera motion direction.

Connected through hyperedge HE-1 (induced by source paper "fast loop closure detection via binary content"):
  with members:
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "convolutional neural network based image representation for visual loop closure detection"
    - "pcanet a simple deep learning baseline for image classification"
    - "return of the devil in the details delving deep into convolutional nets"

Connected through hyperedge HE-2 (induced by source paper "gn net the gauss newton loss for multi weather relocalization"):
  with members:
    - "regnet learning the optimization of direct image to image pose registration"
    - "open3d a modern library for 3d data processing"
    - "neighbourhood consensus networks"
    - "adversarial training for adverse conditions robust metric localisation using appearance transfer"
    - "taking a deeper look at the inverse compositional algorithm"
    - "stereo dso large scale direct sparse visual odometry with stereo cameras"
    - "learning based image enhancement for visual odometry in challenging hdr environments"
    - "superpoint self supervised interest point detection and description"

Connected through hyperedge HE-3 (induced by source paper "gslam a general slam framework and benchmark"):
  with members:
    - "direct sparse visual inertial odometry using dynamic marginalization"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "ldso direct sparse odometry with loop closure"
    - "high quality monocular depth estimation via transfer learning"
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "the tum vi benchmark for evaluating visual inertial odometry"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "topometric localization with deep learning"

Connected through hyperedge HE-4 (induced by source paper "characterizing slam benchmarks and methods for the robust perception age"):
  with members:
    - "good feature matching towards accurate robust vo vslam with low latency"
    - "low latency visual slam with appearance enhanced local map building"
    - "good feature selection for least squares pose optimization in vo vslam"
    - "interiornet mega scale multi sensor photo realistic indoor scenes dataset"
    - "the blackbird dataset a large scale dataset for uav perception in aggressive flight"
    - "the tum vi benchmark for evaluating visual inertial odometry"
    - "direct sparse odometry"
    - "past present and future of simultaneous localization and mapping toward the robust perception age"

Connected through hyperedge HE-5 (induced by source paper "flownorm a learning based method for increasing convergence range of direct alignment"):
  with members:
    - "gn net the gauss newton loss for multi weather relocalization"
    - "accurate optical flow via direct cost volume processing"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "photometric bundle adjustment for vision based slam"
    - "direct sparse odometry"
    - "epicflow edge preserving interpolation of correspondences for optical flow"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "a unified formulation for visual odometry"):
  with members:
    - "loosely coupled semi direct monocular slam"
    - "fdmo feature assisted direct monocular odometry"
    - "ldso direct sparse odometry with loop closure"
    - "challenges in monocular visual odometry photometric calibration motion bias and rolling shutter effect"
    - "keyframe based monocular slam"
    - "direct sparse odometry"
    - "orb slam a versatile and accurate monocular slam system"

Connected through hyperedge HE-7 (induced by source paper "loosely coupled semi direct monocular slam"):
  with members:
    - "ldso direct sparse odometry with loop closure"
    - "challenges in monocular visual odometry photometric calibration motion bias and rolling shutter effect"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "direct sparse odometry"
    - "orb slam a versatile and accurate monocular slam system"

Connected through hyperedge HE-8 (induced by source paper "visual appearance analysis of forest scenes for monocular slam"):
  with members:
    - "challenges in monocular visual odometry photometric calibration motion bias and rolling shutter effect"
    - "toward low flying autonomous mav trail navigation using deep neural networks for environmental awareness"
    - "direct sparse odometry"
    - "orb slam a versatile and accurate monocular slam system"

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)