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: "distributed mapping with privacy and communication constraints lightweight algorithms and object based models"
  Abstract: We consider the following problem: a team of robots is deployed in an unknown environment and it has to collaboratively build a map of the area without a reliable infrastructure for communication. The backbone for modern mapping techniques is pose graph optimization, which estimates the trajectory of the robots, from which the map can be easily built. The first contribution of this paper is a set of distributed algorithms for pose graph optimization: rather than sending all sensor data to a remote sensor fusion server, the robots exchange very partial and noisy information to reach an agreement on the pose graph configuration. Our approach can be considered as a distributed implementation of the two-stage approach of Carlone et al., where we use the Successive Over-Relaxation (SOR) and the Jacobi Over-Relaxation (JOR) as workhorses to split the computation among the robots. As a second contribution, we extend %and demonstrate the applicability of the proposed distributed algorithms to work with object-based map models. The use of object-based models avoids the exchange of raw sensor measurements (e.g., point clouds) further reducing the communication burden. Our third contribution is an extensive experimental evaluation of the proposed techniques, including tests in realistic Gazebo simulations and field experiments in a military test facility. Abundant experimental evidence suggests that one of the proposed algorithms (the Distributed Gauss-Seidel method or DGS) has excellent performance. The DGS requires minimal information exchange, has an anytime flavor, scales well to large teams, is robust to noise, and is easy to implement. Our field tests show that the combined use of our distributed algorithms and object-based models reduces the communication requirements by several orders of magnitude and enables distributed mapping with large teams of robots in real-world problems.

Connected through hyperedge HE-1 (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"
    - "data efficient decentralized visual slam"
    - "netvlad cnn architecture for weakly supervised place recognition"

Connected through hyperedge HE-2 (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"
    - "data efficient decentralized visual slam"
    - "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-3 (induced by source paper "futuremapping 2 gaussian belief propagation for spatial ai"):
  with members:
    - "visual inertial mapping with non linear factor recovery"
    - "scenecode monocular dense semantic reconstruction using learned encoded scene representations"
    - "codeslam learning a compact optimisable representation for dense visual slam"
    - "just in time reconstruction inpainting sparse maps using single view depth predictors as priors"
    - "futuremapping the computational structure of spatial ai systems"
    - "geometric deep learning going beyond euclidean data"
    - "semanticfusion dense 3d semantic mapping with convolutional neural networks"

Connected through hyperedge HE-4 (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"
    - "data efficient decentralized visual slam"
    - "attention and anticipation in fast visual inertial navigation"
    - "netvlad cnn architecture for weakly supervised place recognition"

Connected through hyperedge HE-5 (induced by source paper "resource aware algorithms for distributed loop closure detection with provable performance guarantees"):
  with members:
    - "near optimal budgeted data exchange for distributed loop closure detection"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "data efficient decentralized visual slam"
    - "designing sparse reliable pose graph slam a graph theoretic approach"

Connected through hyperedge HE-6 (induced by source paper "active rendezvous for multi robot pose graph optimization using sensing over wi fi"):
  with members:
    - "convex relaxations for pose graph optimization with outliers"
    - "past present and future of simultaneous localization and mapping toward the robust perception age"

Connected through hyperedge HE-7 (induced by source paper "collaborative large scale dense 3d reconstruction with online inter agent pose optimisation"):
  with members:
    - "on the fly adaptation of regression forests for online camera relocalisation"
    - "infinitam v3 a framework for large scale 3d reconstruction with loop closure"
    - "exploiting 2d floorplan for building scale panorama rgbd alignment"
    - "data efficient decentralized visual slam"
    - "semanticpaint interactive 3d labeling and learning at your fingertips"
    - "a framework for the volumetric integration of depth images"

Connected through hyperedge HE-8 (induced by source paper "near optimal budgeted data exchange for distributed loop closure detection"):
  with members:
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"

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)