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: "deep learning features at scale for visual place recognition"
  Abstract: The success of deep learning techniques in the computer vision domain has triggered a range of initial investigations into their utility for visual place recognition, all using generic features from networks that were trained for other types of recognition tasks. In this paper, we train, at large scale, two CNN architectures for the specific place recognition task and employ a multi-scale feature encoding method to generate condition- and viewpoint-invariant features. To enable this training to occur, we have developed a massive Specific PlacEs Dataset (SPED) with hundreds of examples of place appearance change at thousands of different places, as opposed to the semantic place type datasets currently available. This new dataset enables us to set up a training regime that interprets place recognition as a classification problem. We comprehensively evaluate our trained networks on several challenging benchmark place recognition datasets and demonstrate that they achieve an average 10% increase in performance over other place recognition algorithms and pre-trained CNNs. By analyzing the network responses and their differences from pre-trained networks, we provide insights into what a network learns when training for place recognition, and what these results signify for future research in this area.

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"
    - "lost appearance invariant place recognition for opposite viewpoints using visual semantics"
    - "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 "hierarchical multi process fusion for visual place recognition"):
  with members:
    - "multi process fusion visual place recognition using multiple image processing methods"
    - "leveraging deep visual descriptors for hierarchical efficient localization"
    - "netvlad cnn architecture for weakly supervised place recognition"

Connected through hyperedge HE-5 (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-6 (induced by source paper "attention aware age agnostic visual place recognition"):
  with members:
    - "deep cnn based multi task learning for open set recognition"
    - "discriminative label consistent domain adaptation"
    - "open set domain adaptation by backpropagation"
    - "attention based pyramid aggregation network for visual place recognition"
    - "deep attentional structured representation learning for visual recognition"
    - "from coarse to fine robust hierarchical localization at large scale"
    - "deep adversarial attention alignment for unsupervised domain adaptation the benefit of target expectation maximization"
    - "attention based ensemble for deep metric learning"

Connected through hyperedge HE-7 (induced by source paper "from visual place recognition to navigation learning sample efficient control policies across diverse real world environments"):
  with members:
    - "nuscenes a multimodal dataset for autonomous driving"
    - "multi process fusion visual place recognition using multiple image processing methods"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "touchdown natural language navigation and spatial reasoning in visual street environments"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "active neural localization"
    - "learning to navigate in cities without a map"
    - "learning deployable navigation policies at kilometer scale from a single traversal"

Connected through hyperedge HE-8 (induced by source paper "learning whole image descriptors for real time loop detection andkidnap recovery under large viewpoint difference"):
  with members:
    - "ibow lcd an appearance based loop closure detection approach using incremental bags of binary words"
    - "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"

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.CV(Computer Vision and Pattern Recognition)