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: "lost appearance invariant place recognition for opposite viewpoints using visual semantics"
  Abstract: Human visual scene understanding is so remarkable that we are able to recognize a revisited place when entering it from the opposite direction it was first visited, even in the presence of extreme variations in appearance. This capability is especially apparent during driving: a human driver can recognize where they are when travelling in the reverse direction along a route for the first time, without having to turn back and look. The difficulty of this problem exceeds any addressed in past appearance- and viewpoint-invariant visual place recognition (VPR) research, in part because large parts of the scene are not commonly observable from opposite directions. Consequently, as shown in this paper, the precision-recall performance of current state-of-the-art viewpoint- and appearance-invariant VPR techniques is orders of magnitude below what would be usable in a closed-loop system. Current engineered solutions predominantly rely on panoramic camera or LIDAR sensing setups; an eminently suitable engineering solution but one that is clearly very different to how humans navigate, which also has implications for how naturally humans could interact and communicate with the navigation system. In this paper we develop a suite of novel semantic- and appearance-based techniques to enable for the first time high performance place recognition in this challenging scenario. We first propose a novel Local Semantic Tensor (LoST) descriptor of images using the convolutional feature maps from a state-of-the-art dense semantic segmentation network. Then, to verify the spatial semantic arrangement of the top matching candidates, we develop a novel approach for mining semantically-salient keypoint correspondences.

Connected through hyperedge HE-1 (induced by source paper "mvp unified motion and visual self supervised learning for large scale robotic navigation"):
  with members:
    - "badgr an autonomous self supervised learning based navigation system"
    - "kidnapped radar topological radar localisation using rotationally invariant metric learning"
    - "a hybrid compact neural architecture for visual place recognition"
    - "levelling the playing field a comprehensive comparison of visual place recognition approaches under changing conditions"
    - "beyond photometric loss for self supervised ego motion estimation"
    - "towards navigation without precise localization weakly supervised learning of goal directed navigation cost map"
    - "the oxford radar robotcar dataset a radar extension to the oxford robotcar dataset"
    - "self supervised learning of depth and motion under photometric inconsistency"

Connected through hyperedge HE-2 (induced by source paper "citylearn diverse real world environments for sample efficient navigation policy learning"):
  with members:
    - "a hybrid compact neural architecture for visual place recognition"
    - "levelling the playing field a comprehensive comparison of visual place recognition approaches under changing conditions"
    - "learning to follow directions in street view"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "visual place recognition for aerial robotics exploring accuracy computation trade off for local image descriptors"
    - "cross view policy learning for street navigation"
    - "automatic coverage selection for surface based visual localization"
    - "the streetlearn environment and dataset"

Connected through hyperedge HE-3 (induced by source paper "a hybrid compact neural architecture for visual place recognition"):
  with members:
    - "multi process fusion visual place recognition using multiple image processing methods"
    - "what can neural networks reason about"
    - "dashnet a hybrid artificial and spiking neural network for high speed object tracking"
    - "deep learning features at scale for visual place recognition"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "refinenet multi path refinement networks for high resolution semantic segmentation"
    - "large scale image retrieval with attentive deep local features"
    - "learning to navigate in complex environments"

Connected through hyperedge HE-4 (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"
    - "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"
    - "x view graph based semantic multi view localization"
    - "netvlad cnn architecture for weakly supervised place recognition"

Connected through hyperedge HE-5 (induced by source paper "fine grained segmentation networks self supervised segmentation for improved long term visual localization"):
  with members:
    - "sparse to dense hypercolumn matching for long term visual localization"
    - "understanding the limitations of cnn based absolute camera pose regression"
    - "a cross season correspondence dataset for robust semantic segmentation"
    - "deep clustering for unsupervised learning of visual features"
    - "vlocnet deep multitask learning for semantic visual localization and odometry"
    - "from coarse to fine robust hierarchical localization at large scale"
    - "unsupervised representation learning by predicting image rotations"
    - "vlase vehicle localization by aggregating semantic edges"

Connected through hyperedge HE-6 (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"
    - "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"
    - "x view graph based semantic multi view localization"
    - "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-7 (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"
    - "quadricslam dual quadrics from object detections as landmarks in object oriented slam"

Connected through hyperedge HE-8 (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"
    - "lightweight unsupervised deep loop closure"
    - "deep learning features at scale for visual place recognition"
    - "data efficient decentralized visual slam"
    - "past present and future of simultaneous localization and mapping toward the robust perception age"
    - "visual place recognition using landmark distribution descriptors"

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)