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: "modeling perceptual aliasing in slam via discrete continuous graphical models"
  Abstract: Perceptual aliasing is one of the main causes of failure for Simultaneous Localization and Mapping (SLAM) systems operating in the wild. Perceptual aliasing is the phenomenon where different places generate a similar visual (or, in general, perceptual) footprint. This causes spurious measurements to be fed to the SLAM estimator, which typically results in incorrect localization and mapping results. The problem is exacerbated by the fact that those outliers are highly correlated, in the sense that perceptual aliasing creates a large number of mutually-consistent outliers. Another issue stems from the fact that most state-of-the-art techniques rely on a given trajectory guess (e.g., from odometry) to discern between inliers and outliers and this makes the resulting pipeline brittle, since the accumulation of error may result in incorrect choices and recovery from failures is far from trivial. This work provides a unified framework to model perceptual aliasing in SLAM and provides practical algorithms that can cope with outliers without relying on any initial guess. We present two main contributions. The first is a Discrete-Continuous Graphical Model (DC-GM) for SLAM: the continuous portion of the DC-GM captures the standard SLAM problem, while the discrete portion describes the selection of the outliers and models their correlation. The second contribution is a semidefinite relaxation to perform inference in the DC-GM that returns estimates with provable sub-optimality guarantees. Experimental results on standard benchmarking datasets show that the proposed technique compares favorably with state-of-the-art methods while not relying on an initial guess for optimization.

Connected through hyperedge HE-1 (induced by source paper "teaser fast and certifiable point cloud registration"):
  with members:
    - "did you miss the sign a false negative alarm system for traffic sign detectors"
    - "consensus maximization tree search revisited"
    - "a polynomial time solution for robust registration with extreme outlier rates"
    - "a practical maximum clique algorithm for matching with pairwise constraints"
    - "pointnetlk robust efficient point cloud registration using pointnet"
    - "deep closest point learning representations for point cloud registration"
    - "prnet self supervised learning for partial to partial registration"
    - "fast and globally optimal rigid registration of 3d point sets by transformation decomposition"

Connected through hyperedge HE-2 (induced by source paper "statistical outlier identification in multi robot visual slam using expectation maximization"):
  with members:
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "past present and future of simultaneous localization and mapping toward the robust perception age"

Connected through hyperedge HE-3 (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"
    - "efficient constellation based map merging for semantic slam"
    - "lost appearance invariant place recognition for opposite viewpoints using visual semantics"
    - "quadricslam dual quadrics from object detections as landmarks in object oriented slam"

Connected through hyperedge HE-4 (induced by source paper "in perfect shape certifiably optimal 3d shape reconstruction from 2d landmarks"):
  with members:
    - "towards robust monocular depth estimation mixing datasets for zero shot cross dataset transfer"
    - "a polynomial time solution for robust registration with extreme outlier rates"
    - "learning to reconstruct 3d human pose and shape via model fitting in the loop"
    - "convolutional mesh regression for single image human shape reconstruction"
    - "guaranteed globally optimal planar pose graph and landmark slam via sparse bounded sums of squares programming"
    - "sparse representation for 3d shape estimation a convex relaxation approach"
    - "lifting from the deep convolutional 3d pose estimation from a single image"
    - "posecnn a convolutional neural network for 6d object pose estimation in cluttered scenes"

Connected through hyperedge HE-5 (induced by source paper "a polynomial time solution for robust registration with extreme outlier rates"):
  with members:
    - "fast and globally optimal rigid registration of 3d point sets by transformation decomposition"
    - "guaranteed outlier removal for point cloud registration with correspondences"
    - "segicp integrated deep semantic segmentation and pose estimation"
    - "rfm slam exploiting relative feature measurements to separate orientation and position estimation in slam"
    - "gogma globally optimal gaussian mixture alignment"
    - "multi view self supervised deep learning for 6d pose estimation in the amazon picking challenge"
    - "go icp a globally optimal solution to 3d icp point set registration"
    - "fast algorithms for the maximum clique problem on massive graphs with applications to overlapping community detection"

Connected through hyperedge HE-6 (induced by source paper "graduated non convexity for robust spatial perception from non minimal solvers to global outlier rejection"):
  with members:
    - "a polynomial time solution for robust registration with extreme outlier rates"
    - "cvxpnpl a unified convex solution to the absolute pose estimation problem from point and line correspondences"
    - "convex relaxations for pose graph optimization with outliers"
    - "accelerated inference in markov random fields via smooth riemannian optimization"
    - "guaranteed outlier removal for point cloud registration with correspondences"
    - "robust fitting in computer vision easy or hard"
    - "rotation averaging and strong duality"
    - "sparse representation for 3d shape estimation a convex relaxation approach"

Connected through hyperedge HE-7 (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"
    - "talk resource efficiently to me optimal communication planning for distributed loop closure detection"
    - "data efficient decentralized visual slam"
    - "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-8 (induced by source paper "robust data association for object level semantic slam"):
  with members:
    - "cubeslam monocular 3 d object slam"
    - "monocular object and plane slam in structured environments"
    - "quadricslam dual quadrics from object detections as landmarks in object oriented slam"
    - "frustum pointnets for 3d object detection from rgb d data"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "you only look once unified real time object detection"
    - "efficient pattern matching on binary strings"

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)