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: "automated map reading image based localisation in 2 d maps using binary semantic descriptors"
  Abstract: We describe a novel approach to image based localisation in urban environments using semantic matching between images and a 2-D map. It contrasts with the vast majority of existing approaches which use image to image database matching. We use highly compact binary descriptors to represent semantic features at locations, significantly increasing scalability compared with existing methods and having the potential for greater invariance to variable imaging conditions. The approach is also more akin to human map reading, making it more suited to human-system interaction. The binary descriptors indicate the presence or not of semantic features relating to buildings and road junctions in discrete viewing directions. We use CNN classifiers to detect the features in images and match descriptor estimates with a database of location tagged descriptors derived from the 2-D map. In isolation, the descriptors are not sufficiently discriminative, but when concatenated sequentially along a route, their combination becomes highly distinctive and allows localisation even when using non-perfect classifiers. Performance is further improved by taking into account left or right turns over a route. Experimental results obtained using Google StreetView and OpenStreetMap data show that the approach has considerable potential, achieving localisation accuracy of around 85% using routes corresponding to approximately 200 meters.

Connected through hyperedge HE-1 (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"
    - "in defense of the triplet loss for person re identification"
    - "learning from maps visual common sense for autonomous driving"
    - "find your way by observing the sun and other semantic cues"
    - "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-2 (induced by source paper "end to end learning of driving models with surround view cameras and route planners"):
  with members:
    - "semantic foggy scene understanding with synthetic data"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "domain adaptive faster r cnn for object detection in the wild"
    - "dark model adaptation semantic image segmentation from daytime to nighttime"
    - "event based vision meets deep learning on steering prediction for self driving cars"
    - "failure prediction for autonomous driving"
    - "computer vision for autonomous vehicles problems datasets and state of the art"
    - "carla an open urban driving simulator"

Connected through hyperedge HE-3 (induced by source paper "a multi stage multi task neural network for aerial scene interpretation and geolocalization"):
  with members:
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "multi task learning for segmentation of building footprints with deep neural networks"
    - "an overview of multi task learning in deep neural networks"
    - "pixels to graphs by associative embedding"
    - "effective use of dilated convolutions for segmenting small object instances in remote sensing imagery"
    - "pixelnet representation of the pixels by the pixels and for the pixels"
    - "a review on deep learning techniques applied to semantic segmentation"
    - "mask r cnn"

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)