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: "ibow lcd an appearance based loop closure detection approach using incremental bags of binary words"
  Abstract: In this letter, we introduce iBoW-LCD, a novel appearance-based loop-closure detection method. The presented approach makes use of an incremental bag-of-words (BoW) scheme based on binary descriptors to retrieve previously seen similar images, avoiding any vocabulary training stage usually required by classic BoW models. In addition, to detect loop closures, iBoW-LCD builds on the concept of  dynamic islands , a simple but effective mechanism to group similar images close in time, which reduces the computational times typically associated with Bayesian frameworks. Our approach is validated using several indoor and outdoor public datasets, taken under different environmental conditions, achieving a high accuracy and outperforming other state-of-the-art solutions.

Connected through hyperedge HE-1 (induced by source paper "learning whole image descriptors for real time loop detection andkidnap recovery under large viewpoint difference"):
  with members:
    - "the limits and potentials of deep learning for robotics"
    - "a holistic visual place recognition approach using lightweight cnns for severe viewpoint and appearance changes"
    - "maplab an open framework for research in visual inertial mapping and localization"
    - "neighbourhood consensus networks"
    - "relocalization global optimization and map merging for monocular visual inertial slam"
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "inloc indoor visual localization with dense matching and view synthesis"
    - "lightweight unsupervised deep loop closure"

Connected through hyperedge HE-2 (induced by source paper "fault diagnosing slam for varying scale change detection"):
  with members:
    - "multi attention recurrent network for human communication comprehension"
    - "map management for efficient long term visual localization in outdoor environments"
    - "learning long range perception using self supervision from short range sensors and odometry"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "detection by localization maintenance free change object detector"
    - "lightweight unsupervised deep loop closure"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "yolo9000 better faster stronger"

Connected through hyperedge HE-3 (induced by source paper "mining minimal map segments for visual place classifiers"):
  with members:
    - "large scale object mining for object discovery from unlabeled video"
    - "yolov3 an incremental improvement"
    - "video object segmentation using teacher student adaptation in a human robot interaction hri setting"
    - "soft nms improving object detection with one line of code"
    - "a method to segment maps from different modalities using free space layout maoris map of ripples segmentation"
    - "track then decide category agnostic vision based multi object tracking"
    - "x view graph based semantic multi view localization"
    - "you only look once unified real time object detection"

Connected through hyperedge HE-4 (induced by source paper "simultaneous detection of loop closures and changed objects"):
  with members:
    - "real world anomaly detection in surveillance videos"
    - "detection by localization maintenance free change object detector"
    - "lightweight unsupervised deep loop closure"
    - "yolo9000 better faster stronger"
    - "self localization from images with small overlap"

Connected through hyperedge HE-5 (induced by source paper "long term vehicle localization by recursive knowledge distillation"):
  with members:
    - "deepvo towards end to end visual odometry with deep recurrent convolutional neural networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "loop closure detection with rgb d feature pyramid siamese networks"):
  with members:
    - "cosface large margin cosine loss for deep face recognition"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "fine tuning cnn image retrieval with no human annotation"
    - "improving generalization performance by switching from adam to sgd"
    - "deep image retrieval learning global representations for image search"
    - "yolo9000 better faster stronger"
    - "densely connected convolutional networks"
    - "feature pyramid networks for object detection"

Connected through hyperedge HE-7 (induced by source paper "detection by localization maintenance free change object detector"):
  with members:
    - "yolov3 an incremental improvement"
    - "map management for efficient long term visual localization in outdoor environments"
    - "revisiting oxford and paris large scale image retrieval benchmarking"
    - "lightweight unsupervised deep loop closure"
    - "driven to distraction self supervised distractor learning for robust monocular visual odometry in urban environments"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "learning deep structured network for weakly supervised change detection"
    - "yolo9000 better faster stronger"

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)