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: "semantic image based geolocation given a map"
  Abstract: The problem visual place recognition is commonly used strategy for localization. Most successful appearance based methods typically rely on a large database of views endowed with local or global image descriptors and strive to retrieve the views of the same location. The quality of the results is often affected by the density of the reference views and the robustness of the image representation with respect to viewpoint variations, clutter and seasonal changes. In this work we present an approach for geo-locating a novel view and determining camera location and orientation using a map and a sparse set of geo-tagged reference views. We propose a novel technique for detection and identification of building facades from geo-tagged reference view using the map and geometry of the building facades. We compute the likelihood of camera location and orientation of the query images using the detected landmark (building) identities from reference views, 2D map of the environment, and geometry of building facades. We evaluate our approach for building identification and geo-localization on a new challenging outdoors urban dataset exhibiting large variations in appearance and viewpoint.

Connected through hyperedge HE-1 (induced by source paper "lending orientation to neural networks for cross view geo localization"):
  with members:
    - "understanding the limitations of cnn based absolute camera pose regression"
    - "deep stochastic attraction and repulsion embedding for image based localization"
    - "cross view image synthesis using conditional gans"
    - "cross view image matching for geo localization in urban environments"
    - "deep complex networks"
    - "deep metric learning with angular loss"
    - "on complex valued convolutional neural networks"
    - "localizing and orienting street views using overhead imagery"

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"
    - "find your way by observing the sun and other semantic cues"
    - "localizing and orienting street views using overhead imagery"
    - "learning deep structure preserving image text embeddings"

Connected through hyperedge HE-3 (induced by source paper "optimal feature transport for cross view image geo localization"):
  with members:
    - "lending orientation to neural networks for cross view geo localization"
    - "feature selection for unsupervised domain adaptation using optimal transport"
    - "deeppermnet visual permutation learning"
    - "localizing and orienting street views using overhead imagery"
    - "predicting ground level scene layout from aerial imagery"
    - "semantic cross view matching"
    - "optimal transport for domain adaptation"
    - "spatial transformer networks"

Connected through hyperedge HE-4 (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"
    - "find your way by observing the sun and other semantic cues"
    - "visual place recognition using landmark distribution descriptors"
    - "multi track map matching"

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)