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: "slam with objects using a nonparametric pose graph"
  Abstract: Mapping and self-localization in unknown environments are fundamental capabilities in many robotic applications. These tasks typically involve the identification of objects as unique features or landmarks, which requires the objects both to be detected and then assigned a unique identifier that can be maintained when viewed from different perspectives and in different images. The \textit{data association} and \textit{simultaneous localization and mapping} (SLAM) problems are, individually, well-studied in the literature. But these two problems are inherently tightly coupled, and that has not been well-addressed. Without accurate SLAM, possible data associations are combinatorial and become intractable easily. Without accurate data association, the error of SLAM algorithms diverge easily. This paper proposes a novel nonparametric pose graph that models data association and SLAM in a single framework. An algorithm is further introduced to alternate between inferring data association and performing SLAM. Experimental results show that our approach has the new capability of associating object detections and localizing objects at the same time, leading to significantly better performance on both the data association and SLAM problems than achieved by considering only one and ignoring imperfections in the other.

Connected through hyperedge HE-1 (induced by source paper "deep slam object level rgbd slam based on class specific deep shape priors"):
  with members:
    - "dense object reconstruction from rgbd images with embedded deep shape representations"
    - "fusion volumetric object level slam"
    - "real time progressive 3d semantic segmentation for indoor scene"
    - "scan2cad learning cad model alignment in rgb d scans"
    - "pix3d dataset and methods for single image 3d shape modeling"
    - "incremental object database building 3d models from multiple partial observations"
    - "octnetfusion learning depth fusion from data"
    - "octree generating networks efficient convolutional architectures for high resolution 3d outputs"

Connected through hyperedge HE-2 (induced by source paper "gradslam dense slam meets automatic differentiation"):
  with members:
    - "end to end lane detection through differentiable least squares fitting"
    - "scenecode monocular dense semantic reconstruction using learned encoded scene representations"
    - "kornia an open source differentiable computer vision library for pytorch"
    - "cubeslam monocular 3 d object slam"
    - "generalized inner loop meta learning"
    - "deeptam deep tracking and mapping"
    - "regnet learning the optimization of direct image to image pose registration"
    - "constructing category specific models for monocular object slam"

Connected through hyperedge HE-3 (induced by source paper "integrating objects into monocular slam line based category specific models"):
  with members:
    - "constructing category specific models for monocular object slam"
    - "cubeslam monocular 3d object detection and slam without prior models"
    - "edge slam edge points based monocular visual slam"
    - "object centric photometric bundle adjustment with deep shape prior"
    - "stacked hourglass networks for human pose estimation"
    - "ssd single shot multibox detector"
    - "you only look once unified real time object detection"
    - "shapenet an information rich 3d model repository"

Connected through hyperedge HE-4 (induced by source paper "network uncertainty informed semantic feature selection for visual slam"):
  with members:
    - "utilizing semantic visual landmarks for precise vehicle navigation"
    - "long term visual localization using semantically segmented images"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "semi dense 3d semantic mapping from monocular slam"
    - "modelling uncertainty in deep learning for camera relocalization"
    - "bayesian segnet model uncertainty in deep convolutional encoder decoder architectures for scene understanding"

Connected through hyperedge HE-5 (induced by source paper "probabilistic data association via mixture models for robust semantic slam"):
  with members:
    - "long duration fully autonomous operation of rotorcraft unmanned aerial systems for remote sensing data acquisition"
    - "convex relaxations for pose graph optimization with outliers"
    - "modeling perceptual aliasing in slam via discrete continuous graphical models"
    - "cubeslam monocular 3d object detection and slam without prior models"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "lift learned invariant feature transform"
    - "ssd single shot multibox detector"
    - "speed accuracy trade offs for modern convolutional object detectors"

Connected through hyperedge HE-6 (induced by source paper "constructing category specific models for monocular object slam"):
  with members:
    - "6 dof object pose from semantic keypoints"
    - "stacked hourglass networks for human pose estimation"
    - "meaningful maps with object oriented semantic mapping"
    - "yolo9000 better faster stronger"
    - "reconstructing vechicles from a single image shape priors for road scene understanding"
    - "shapenet an information rich 3d model repository"
    - "towards scene understanding with detailed 3d object representations"
    - "fast r cnn"

Connected through hyperedge HE-7 (induced by source paper "efficient constellation based map merging for semantic slam"):
  with members:
    - "quadricslam dual quadrics from object detections as landmarks in object oriented slam"
    - "ssd single shot multibox detector"
    - "past present and future of simultaneous localization and mapping toward the robust perception age"
    - "you only look once unified real time object detection"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-8 (induced by source paper "uav visual teach and repeat using only semantic object features"):
  with members:
    - "ssd single shot multibox detector"
    - "yolo9000 better faster stronger"
    - "place categorization and semantic mapping on a mobile robot"
    - "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)