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: "dual quadrics from object detection boundingboxes as landmark representations in slam"
  Abstract: Research in Simultaneous Localization And Mapping (SLAM) is increasingly moving towards richer world representations involving objects and high level features that enable a semantic model of the world for robots, potentially leading to a more meaningful set of robot-world interactions. Many of these advances are grounded in state-of-the-art computer vision techniques primarily developed in the context of image-based benchmark datasets, leaving several challenges to be addressed in adapting them for use in robotics. In this paper, we derive a formulation for Simultaneous Localization And Mapping (SLAM) that uses dual quadrics as 3D landmark representations, and show how 2D bounding boxes (such as those typically obtained from visual object detection systems) can directly constrain the quadric parameters. Our paper demonstrates how to jointly estimate the robot pose and dual quadric parameters in factor graph based SLAM with a general perspective camera, and covers the use-cases of a robot moving with a monocular camera with and without the availability of additional depth information.

Connected through hyperedge HE-1 (induced by source paper "real time monocular object model aware sparse slam"):
  with members:
    - "structure aware slam using quadrics and planes"
    - "planenet piece wise planar reconstruction from a single rgb image"
    - "yolov3 an incremental improvement"
    - "quadricslam dual quadrics from object detections as landmarks in object oriented slam"
    - "real time joint semantic segmentation and depth estimation using asymmetric annotations"
    - "infinitam v3 a framework for large scale 3d reconstruction with loop closure"
    - "pop up slam semantic monocular plane slam for low texture environments"
    - "meaningful maps with object oriented semantic mapping"

Connected through hyperedge HE-2 (induced by source paper "quadricslam dual quadrics from object detections as landmarks in object oriented slam"):
  with members:
    - "yolov3 an incremental improvement"
    - "dropout sampling for robust object detection in open set conditions"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "mask r cnn"
    - "ssd single shot multibox detector"
    - "semanticfusion dense 3d semantic mapping with convolutional neural networks"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "orb slam a versatile and accurate monocular slam system"

Connected through hyperedge HE-3 (induced by source paper "structure aware slam using quadrics and planes"):
  with members:
    - "pop up slam semantic monocular plane slam for low texture environments"
    - "meaningful maps with object oriented semantic mapping"
    - "direct sparse odometry"
    - "semanticfusion dense 3d semantic mapping with convolutional neural networks"
    - "past present and future of simultaneous localization and mapping toward the robust perception age"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "orb slam a versatile and accurate monocular slam system"
    - "predicting depth surface normals and semantic labels with a common multi scale convolutional architecture"

Connected through hyperedge HE-4 (induced by source paper "dropout sampling for robust object detection in open set conditions"):
  with members:
    - "the extreme value machine"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "yolo9000 better faster stronger"
    - "modelling uncertainty in deep learning for camera relocalization"
    - "towards open set deep networks"
    - "bayesian segnet model uncertainty in deep convolutional encoder decoder architectures for scene understanding"
    - "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.RO(Robotics)