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: "don t look back robustifying place categorization for viewpoint and condition invariant place recognition"
  Abstract: When a human drives a car along a road for the first time, they later recognize where they are on the return journey typically without needing to look in their rear-view mirror or turn around to look back, despite significant viewpoint and appearance change. Such navigation capabilities are typically attributed to our semantic visual understanding of the environment [1] beyond geometry to recognizing the types of places we are passing through such as "passing a shop on the left" or "moving through a forested area". Humans are in effect using place categorization [2] to perform specific place recognition even when the viewpoint is 180 degrees reversed. Recent advances in deep neural networks have enabled high-performance semantic understanding of visual places and scenes, opening up the possibility of emulating what humans do. In this work, we develop a novel methodology for using the semantics-aware higher-order layers of deep neural networks for recognizing specific places from within a reference database. To further improve the robustness to appearance change, we develop a descriptor normalization scheme that builds on the success of normalization schemes for pure appearance-based techniques such as SeqSLAM [3]. Using two different datasets - one road-based, one pedestrian-based, we evaluate the performance of the system in performing place recognition on reverse traversals of a route with a limited field of view camera and no turn-back-and-look behaviours, and compare to existing state-of-the-art techniques and vanilla off-the-shelf features. The results demonstrate significant improvements over the existing state of the art, especially for extreme perceptual challenges that involve both great viewpoint change and environmental appearance change. We also provide experimental analyses of the contributions of the various system components.

Connected through hyperedge HE-1 (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-2 (induced by source paper "unsupervised learning methods for visual place recognition in discretely and continuously changing environments"):
  with members:
    - "night to day image translation for retrieval based localization"
    - "combogan unrestrained scalability for image domain translation"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "large scale image retrieval with attentive deep local features"
    - "netvlad cnn architecture for weakly supervised place recognition"
    - "on the performance of convnet features for 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:
    - "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"
    - "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-4 (induced by source paper "dynamic traffic scene classification with space time coherence"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "the apolloscape open dataset for autonomous driving and its application"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "toward driving scene understanding a dataset for learning driver behavior and causal reasoning"
    - "rethinking the faster r cnn architecture for temporal action localization"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "traffic scene recognition based on deep cnn and vlad spatial pyramids"
    - "knowledge guided disambiguation for large scale scene classification with multi resolution cnns"

Connected through hyperedge HE-5 (induced by source paper "capricorn communication aware place recognition using interpretable constellations of objects in robot networks"):
  with members:
    - "fusion volumetric object level slam"
    - "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"
    - "quadricslam dual quadrics from object detections as landmarks in object oriented slam"

Connected through hyperedge HE-6 (induced by source paper "filter early match late improving network based visual place recognition"):
  with members:
    - "towards compact convnets via structure sparsity regularized filter pruning"
    - "multi process fusion visual place recognition using multiple image processing methods"
    - "lost appearance invariant place recognition for opposite viewpoints using visual semantics"
    - "feature map filtering improving visual place recognition with convolutional calibration"
    - "deep learning features at scale for visual place recognition"
    - "refinenet multi path refinement networks for high resolution semantic segmentation"
    - "large scale image retrieval with attentive deep local features"
    - "pruning filters for efficient convnets"

Connected through hyperedge HE-7 (induced by source paper "multi process fusion visual place recognition using multiple image processing methods"):
  with members:
    - "openseqslam2 0 an open source toolbox for visual place recognition under changing conditions"
    - "lost appearance invariant place recognition for opposite viewpoints using visual semantics"
    - "deep learning features at scale for visual place recognition"
    - "look no further adapting the localization sensory window to the temporal characteristics of the environment"
    - "netvlad cnn architecture for weakly supervised place recognition"
    - "on the performance of convnet features for place recognition"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-8 (induced by source paper "feature map filtering improving visual place recognition with convolutional calibration"):
  with members:
    - "lost appearance invariant place recognition for opposite viewpoints using visual semantics"
    - "interpreting deep visual representations via network dissection"
    - "deep learning features at scale for visual place recognition"
    - "netvlad cnn architecture for weakly supervised place recognition"
    - "facenet a unified embedding for face recognition and clustering"
    - "on the performance of convnet features for place recognition"

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)