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: "on the performance of convnet features for place recognition"
  Abstract: After the incredible success of deep learning in the computer vision domain, there has been much interest in applying Convolutional Network (ConvNet) features in robotic fields such as visual navigation and SLAM. Unfortunately, there are fundamental differences and challenges involved. Computer vision datasets are very different in character to robotic camera data, real-time performance is essential, and performance priorities can be different. This paper comprehensively evaluates and compares the utility of three state-of-the-art ConvNets on the problems of particular relevance to navigation for robots; viewpoint-invariance and condition-invariance, and for the first time enables real-time place recognition performance using ConvNets with large maps by integrating a variety of existing (locality-sensitive hashing) and novel (semantic search space partitioning) optimization techniques. We present extensive experiments on four real world datasets cultivated to evaluate each of the specific challenges in place recognition. The results demonstrate that speed-ups of two orders of magnitude can be achieved with minimal accuracy degradation, enabling real-time performance. We confirm that networks trained for semantic place categorization also perform better at (specific) place recognition when faced with severe appearance changes and provide a reference for which networks and layers are optimal for different aspects of the place recognition problem.

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 "unsupervised learning methods for visual place recognition in discretely and continuously changing environments"):
  with members:
    - "don t look back robustifying place categorization for viewpoint and condition invariant place recognition"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "improving place recognition using dynamic object detection"):
  with members:
    - "fast r cnn"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "orb slam a versatile and accurate monocular slam system"
    - "convolutional neural network based place recognition"
    - "imagenet large scale visual recognition challenge"
    - "microsoft coco common objects in context"

Connected through hyperedge HE-4 (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"
    - "x view graph based semantic multi view localization"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"

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"
    - "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-6 (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"
    - "lost appearance invariant place recognition for opposite viewpoints using visual semantics"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "fault diagnosing slam for varying scale change detection"):
  with members:
    - "ibow lcd an appearance based loop closure detection approach using incremental bags of binary words"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "learning navigation by visual localization and trajectory prediction"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "closing the gap towards end to end autonomous vehicle system"
    - "learning accurate comfortable and human like driving"
    - "a multi stage multi task neural network for aerial scene interpretation and geolocalization"
    - "variational end to end navigation and localization"
    - "learning to navigate in cities without a map"
    - "end to end learning of driving models with surround view cameras and route planners"
    - "geometric loss functions for camera pose regression with deep learning"

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)