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: "data efficient decentralized visual slam"
  Abstract: Decentralized visual simultaneous localization and mapping (SLAM) is a powerful tool for multi-robot applications in environments where absolute positioning systems are not available. Being visual, it relies on cameras, cheap, lightweight and versatile sensors, and being decentralized, it does not rely on communication to a central ground station. In this work, we integrate state-of-the-art decentralized SLAM components into a new, complete decentralized visual SLAM system. To allow for data association and co-optimization, existing decentralized visual SLAM systems regularly exchange the full map data between all robots, incurring large data transfers at a complexity that scales quadratically with the robot count. In contrast, our method performs efficient data association in two stages: in the first stage a compact full-image descriptor is deterministically sent to only one robot. In the second stage, which is only executed if the first stage succeeded, the data required for relative pose estimation is sent, again to only one robot. Thus, data association scales linearly with the robot count and uses highly compact place representations. For optimization, a state-of-the-art decentralized pose-graph optimization method is used. It exchanges a minimum amount of data which is linear with trajectory overlap. We characterize the resulting system and identify bottlenecks in its components. The system is evaluated on publicly available data and we provide open access to the code.

Connected through hyperedge HE-1 (induced by source paper "kidnapped radar topological radar localisation using rotationally invariant metric learning"):
  with members:
    - "masking by moving learning distraction free radar odometry from pose information"
    - "the oxford radar robotcar dataset a radar extension to the oxford robotcar dataset"
    - "making convolutional networks shift invariant again"
    - "radar only ego motion estimation in difficult settings via graph matching"
    - "a holistic visual place recognition approach using lightweight cnns for severe viewpoint and appearance changes"
    - "omnidirectional cnn for visual place recognition and navigation"
    - "pointnetvlad deep point cloud based retrieval for large scale place recognition"
    - "local descriptor for robust place recognition using lidar intensity"

Connected through hyperedge HE-2 (induced by source paper "capricorn communication aware place recognition using interpretable constellations of objects in robot networks"):
  with members:
    - "fusion volumetric object level slam"
    - "don t look back robustifying place categorization for viewpoint and condition invariant place recognition"
    - "structure aware slam using quadrics and planes"
    - "yolov3 an incremental improvement"
    - "real time monocular object model aware sparse slam"
    - "modeling perceptual aliasing in slam via discrete continuous graphical models"
    - "efficient constellation based map merging for semantic slam"
    - "lost appearance invariant place recognition for opposite viewpoints using visual semantics"

Connected through hyperedge HE-3 (induced by source paper "levelling the playing field a comprehensive comparison of visual place recognition approaches under changing conditions"):
  with members:
    - "condition invariant multi view place recognition"
    - "multi process fusion visual place recognition using multiple image processing methods"
    - "a holistic visual place recognition approach using lightweight cnns for severe viewpoint and appearance changes"
    - "lost appearance invariant place recognition for opposite viewpoints using visual semantics"
    - "lightweight unsupervised deep loop closure"
    - "deep learning features at scale for visual place recognition"
    - "past present and future of simultaneous localization and mapping toward the robust perception age"
    - "visual place recognition using landmark distribution descriptors"

Connected through hyperedge HE-4 (induced by source paper "are state of the art visual place recognition techniques any good for aerial robotics"):
  with members:
    - "levelling the playing field a comprehensive comparison of visual place recognition approaches under changing conditions"
    - "a holistic visual place recognition approach using lightweight cnns for severe viewpoint and appearance changes"
    - "memorable maps a framework for re defining places in visual place recognition"
    - "ultimate slam combining events images and imu for robust visual slam in hdr and high speed scenarios"
    - "lightweight unsupervised deep loop closure"
    - "deep learning features at scale for visual place recognition"
    - "past present and future of simultaneous localization and mapping toward the robust perception age"
    - "spatial transformer networks"

Connected through hyperedge HE-5 (induced by source paper "door slam distributed online and outlier resilient slam for robotic teams"):
  with members:
    - "active rendezvous for multi robot pose graph optimization using sensing over wi fi"
    - "a resource aware approach to collaborative loop closure detection with provable performance guarantees"
    - "convex relaxations for pose graph optimization with outliers"
    - "near optimal budgeted data exchange for distributed loop closure detection"
    - "modeling perceptual aliasing in slam via discrete continuous graphical models"
    - "talk resource efficiently to me optimal communication planning for distributed loop closure detection"
    - "distributed mapping with privacy and communication constraints lightweight algorithms and object based models"
    - "netvlad cnn architecture for weakly supervised place recognition"

Connected through hyperedge HE-6 (induced by source paper "search and rescue under the forest canopy using multiple uavs"):
  with members:
    - "segmap 3d segment mapping using data driven descriptors"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "distributed mapping with privacy and communication constraints lightweight algorithms and object based models"
    - "netvlad cnn architecture for weakly supervised place recognition"
    - "multi image matching via fast alternating minimization"
    - "near optimal joint object matching via convex relaxation"
    - "dynamic clustering via asymptotics of the dependent dirichlet process mixture"
    - "efficient informative sensing using multiple robots"

Connected through hyperedge HE-7 (induced by source paper "measurement scheduling for cooperative localization in resource constrained conditions"):
  with members:
    - "cooperative localization under limited connectivity"
    - "a resource aware approach to collaborative loop closure detection with provable performance guarantees"
    - "scheduling nonlinear sensors for stochastic process estimation"
    - "server assisted distributed cooperative localization over unreliable communication links"
    - "cooperative localization for mobile agents a recursive decentralized algorithm based on kalman filter decoupling"

Connected through hyperedge HE-8 (induced by source paper "a resource aware approach to collaborative loop closure detection with provable performance guarantees"):
  with members:
    - "resource aware algorithms for distributed loop closure detection with provable performance guarantees"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "distributed mapping with privacy and communication constraints lightweight algorithms and object based models"
    - "attention and anticipation in fast visual inertial navigation"
    - "netvlad cnn architecture for weakly supervised place recognition"

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)