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: "adversarial training for adverse conditions robust metric localisation using appearance transfer"
  Abstract: We present a method of improving visual place recognition and metric localisation under very strong appear- ance change. We learn an invertable generator that can trans- form the conditions of images, e.g. from day to night, summer to winter etc. This image transforming filter is explicitly designed to aid and abet feature-matching using a new loss based on SURF detector and dense descriptor maps. A network is trained to output synthetic images optimised for feature matching given only an input RGB image, and these generated images are used to localize the robot against a previously built map using traditional sparse matching approaches. We benchmark our results using multiple traversals of the Oxford RobotCar Dataset over a year-long period, using one traversal as a map and the other to localise. We show that this method significantly improves place recognition and localisation under changing and adverse conditions, while reducing the number of mapping runs needed to successfully achieve reliable localisation.

Connected through hyperedge HE-1 (induced by source paper "rsl net localising in satellite images from a radar on the ground"):
  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"
    - "from coarse to fine robust hierarchical localization at large scale"
    - "conditional image to image translation"
    - "learning steerable filters for rotation equivariant cnns"
    - "polar transformer networks"
    - "geometric loss functions for camera pose regression with deep learning"
    - "learning less is more 6d camera localization via 3d surface regression"

Connected through hyperedge HE-2 (induced by source paper "online lidar slam for legged robots with robust registration and deep learned loop closure"):
  with members:
    - "learning to see the wood for the trees deep laser localization in urban and natural environments on a cpu"
    - "segmatch segment based place recognition in 3d point clouds"
    - "orb slam a versatile and accurate monocular slam system"

Connected through hyperedge HE-3 (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"
    - "lost appearance invariant place recognition for opposite viewpoints using visual semantics"
    - "unsupervised representation learning by predicting image rotations"

Connected through hyperedge HE-4 (induced by source paper "don t worry about the weather unsupervised condition dependent domain adaptation"):
  with members:
    - "semantic nighttime image segmentation with synthetic stylized data gradual adaptation and uncertainty aware evaluation"
    - "learning to adapt structured output space for semantic segmentation"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "exemplar guided unsupervised image to image translation with semantic consistency"
    - "map management for efficient long term visual localization in outdoor environments"
    - "domain stylization a strong simple baseline for synthetic to real image domain adaptation"
    - "dark model adaptation semantic image segmentation from daytime to nighttime"
    - "night to day image translation for retrieval based localization"

Connected through hyperedge HE-5 (induced by source paper "generating all the roads to rome road layout randomization for improved road marking segmentation"):
  with members:
    - "end to end lane detection through differentiable least squares fitting"
    - "a system for generating complex physically accurate sensor images for automotive applications"
    - "semantic image synthesis with spatially adaptive normalization"
    - "aads augmented autonomous driving simulation using data driven algorithms"
    - "augmented lidar simulator for autonomous driving"
    - "streetgen in base city scale procedural generation of streets road network road surface and street objects"
    - "3d lanenet end to end 3d multiple lane detection"
    - "vadra visual adversarial domain randomization and augmentation"

Connected through hyperedge HE-6 (induced by source paper "gn net the gauss newton loss for multi weather relocalization"):
  with members:
    - "regnet learning the optimization of direct image to image pose registration"
    - "open3d a modern library for 3d data processing"
    - "neighbourhood consensus networks"
    - "taking a deeper look at the inverse compositional algorithm"
    - "stereo dso large scale direct sparse visual odometry with stereo cameras"
    - "learning based image enhancement for visual odometry in challenging hdr environments"
    - "superpoint self supervised interest point detection and description"
    - "carla an open urban driving simulator"

Connected through hyperedge HE-7 (induced by source paper "multi frame gan image enhancement for stereo visual odometry in low light"):
  with members:
    - "video to video synthesis"
    - "reconet real time coherent video style transfer network"
    - "learning linear transformations for fast arbitrary style transfer"
    - "night to day image translation for retrieval based localization"
    - "deep virtual stereo odometry leveraging deep depth prediction for monocular direct sparse odometry"
    - "drit diverse image to image translation via disentangled representations"
    - "stereo dso large scale direct sparse visual odometry with stereo cameras"
    - "learning based image enhancement for visual odometry in challenging hdr environments"

Connected through hyperedge HE-8 (induced by source paper "scalable place recognition under appearance change for autonomous driving"):
  with members:
    - "semi parametric topological memory for navigation"
    - "dels 3d deep localization and segmentation with a 3d semantic map"
    - "polysemous codes"
    - "vidloc a deep spatio temporal model for 6 dof video clip relocalization"
    - "geometric loss functions for camera pose regression with deep learning"
    - "benchmarking 6dof urban visual localization in changing conditions"
    - "learning less is more 6d camera localization via 3d surface regression"
    - "geometry aware learning of maps for camera localization"

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)