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: "x view graph based semantic multi view localization"
  Abstract: Global registration of multi-view robot data is a challenging task. Appearance-based global localization approaches often fail under drastic view-point changes, as representations have limited view-point invariance. This work is based on the idea that human-made environments contain rich semantics which can be used to disambiguate global localization. Here, we present X-View, a Multi-View Semantic Global Localization system. X-View leverages semantic graph descriptor matching for global localization, enabling localization under drastically different view-points. While the approach is general in terms of the semantic input data, we present and evaluate an implementation on visual data. We demonstrate the system in experiments on the publicly available SYNTHIA dataset, on a realistic urban dataset recorded with a simulator, and on real-world StreetView data. Our findings show that X-View is able to globally localize aerial-to-ground, and ground-to-ground robot data of drastically different view-points. Our approach achieves an accuracy of up to 85 % on global localizations in the multi-view case, while the benchmarked baseline appearance-based methods reach up to 70 %.

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 "fast compact and highly scalable visual place recognition through sequence based matching of overloaded representations"):
  with members:
    - "a hybrid compact neural architecture for visual place recognition"
    - "btel a binary tree encoding approach for visual localization"
    - "link and code fast indexing with graphs and compact regression codes"
    - "lost appearance invariant place recognition for opposite viewpoints using visual semantics"
    - "hbst a hamming distance embedding binary search tree for feature based visual place recognition"
    - "learning to index for nearest neighbor search"
    - "billion scale similarity search with gpus"
    - "netvlad cnn architecture for weakly supervised place recognition"

Connected through hyperedge HE-3 (induced by source paper "look no deeper recognizing places from opposing viewpoints under varying scene appearance using single view depth estimation"):
  with members:
    - "don t look back robustifying place categorization for viewpoint and condition invariant place recognition"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "lost appearance invariant place recognition for opposite viewpoints using visual semantics"
    - "a deeper insight into the undemon unsupervised deep network for depth and ego motion estimation"
    - "eng end to end neural geometry for robust depth and pose estimation using cnns"
    - "pointnetvlad deep point cloud based retrieval for large scale place recognition"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "improving condition and environment invariant place recognition with semantic place categorization"

Connected through hyperedge HE-4 (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-5 (induced by source paper "mining minimal map segments for visual place classifiers"):
  with members:
    - "large scale object mining for object discovery from unlabeled video"
    - "ibow lcd an appearance based loop closure detection approach using incremental bags of binary words"
    - "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"
    - "you only look once unified real time object detection"

Connected through hyperedge HE-6 (induced by source paper "image based place recognition on bucolic environment across seasons from semantic edge description"):
  with members:
    - "devil is in the edges learning semantic boundaries from noisy annotations"
    - "vlase vehicle localization by aggregating semantic edges"
    - "casenet deep category aware semantic edge detection"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "large scale image retrieval with attentive deep local features"
    - "pyramid scene parsing network"
    - "netvlad cnn architecture for weakly supervised place recognition"
    - "neural codes for image retrieval"

Connected through hyperedge HE-7 (induced by source paper "modular sensor fusion for semantic segmentation"):
  with members:
    - "da rnn semantic mapping with data associated recurrent neural networks"
    - "a review on deep learning techniques applied to semantic segmentation"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "choosing smartly adaptive multimodal fusion for object detection in changing environments"
    - "bayesian segnet model uncertainty in deep convolutional encoder decoder architectures for scene understanding"
    - "deep residual learning for image recognition"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"
    - "hands deep in deep learning for hand pose estimation"

Connected through hyperedge HE-8 (induced by source paper "local descriptor for robust place recognition using lidar intensity"):
  with members:
    - "robust photogeometric localization over time for map centric loop closure"
    - "pointnetvlad deep point cloud based retrieval for large scale place recognition"
    - "ground edge based lidar localization without a reflectivity calibration for autonomous driving"
    - "visual place recognition with probabilistic voting"
    - "segmatch segment based place recognition in 3d point clouds"

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)