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: "find your way by observing the sun and other semantic cues"
  Abstract: In this paper we present a robust, efficient and affordable approach to self-localization which does not require neither GPS nor knowledge about the appearance of the world. Towards this goal, we utilize freely available cartographic maps and derive a probabilistic model that exploits semantic cues in the form of sun direction, presence of an intersection, road type, speed limit as well as the ego-car trajectory in order to produce very reliable localization results. Our experimental evaluation shows that our approach can localize much faster (in terms of driving time) with less computation and more robustly than competing approaches, which ignore semantic information.

Connected through hyperedge HE-1 (induced by source paper "shadow transfer single image relighting for urban road scenes"):
  with members:
    - "a cyclically trained adversarial network for invariant representation learning"
    - "an adversarial neuro tensorial approach for learning disentangled representations"
    - "failing to learn autonomously identifying perception failures for self driving cars"
    - "online illumination invariant moving object detection by generative neural network"
    - "night to day image translation for retrieval based localization"
    - "geometric image synthesis"
    - "carla an open urban driving simulator"
    - "combogan unrestrained scalability for image domain translation"

Connected through hyperedge HE-2 (induced by source paper "you are here geolocation by embedding maps and images"):
  with members:
    - "towards navigation without precise localization weakly supervised learning of goal directed navigation cost map"
    - "variational end to end navigation and localization"
    - "automated map reading image based localisation in 2 d maps using binary semantic descriptors"
    - "in defense of the triplet loss for person re identification"
    - "learning from maps visual common sense for autonomous driving"
    - "localizing and orienting street views using overhead imagery"
    - "semantic image based geolocation given a map"
    - "learning deep structure preserving image text embeddings"

Connected through hyperedge HE-3 (induced by source paper "exploiting sparse semantic hd maps for self driving vehicle localization"):
  with members:
    - "deep multi sensor lane detection"
    - "semantic visual localization"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "deep watershed transform for instance segmentation"
    - "torontocity seeing the world with a million eyes"
    - "pyramid scene parsing network"
    - "spatial transformer networks"
    - "netvlad cnn architecture for weakly supervised place recognition"

Connected through hyperedge HE-4 (induced by source paper "auxiliary tasks in multi task learning"):
  with members:
    - "evaluation of cnn based single image depth estimation methods"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "sim4cv a photo realistic simulator for computer vision applications"
    - "procedural modeling and physically based rendering for synthetic data generation in automotive applications"
    - "carla an open urban driving simulator"
    - "an overview of multi task learning in deep neural networks"
    - "playing for benchmarks"
    - "rethinking atrous convolution for semantic image segmentation"

Connected through hyperedge HE-5 (induced by source paper "how to train a cat learning canonical appearance transformations for direct visual localization under illumination change"):
  with members:
    - "learning based image enhancement for visual odometry in challenging hdr environments"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "relative camera pose estimation using convolutional neural networks"
    - "reducing drift in visual odometry by inferring sun direction using a bayesian convolutional neural network"
    - "light source estimation with analytical path tracing"
    - "virtual worlds as proxy for multi object tracking analysis"
    - "direct sparse odometry"
    - "image to image translation with conditional adversarial networks"

Connected through hyperedge HE-6 (induced by source paper "automated map reading image based localisation in 2 d maps using binary semantic descriptors"):
  with members:
    - "learning from maps visual common sense for autonomous driving"
    - "semantic image based geolocation given a map"
    - "visual place recognition using landmark distribution descriptors"
    - "multi track map matching"

Connected through hyperedge HE-7 (induced by source paper "reducing drift in visual odometry by inferring sun direction using a bayesian convolutional neural network"):
  with members:
    - "direct sparse odometry"
    - "probe gk predictive robust estimation using generalized kernels"
    - "improving the accuracy of stereo visual odometry using visual illumination estimation"
    - "modelling uncertainty in deep learning for camera relocalization"
    - "posenet a convolutional network for real time 6 dof camera relocalization"
    - "going deeper with convolutions"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-8 (induced by source paper "multinet real time joint semantic reasoning for autonomous driving"):
  with members:
    - "fast lidar based road detection using fully convolutional neural networks"
    - "hyperface a deep multi task learning framework for face detection landmark localization pose estimation and gender recognition"
    - "ssd single shot multibox detector"
    - "generating images with recurrent adversarial networks"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "what makes for effective detection proposals"
    - "wider or deeper revisiting the resnet model for visual recognition"
    - "weakly and semi supervised learning of a dcnn for semantic image segmentation"

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)