human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "optimal feature transport for cross view image geo localization"):
  Abstract: This paper addresses the problem of cross-view image based localization, where the geographic location of a ground-level street-view query image is estimated by matching it against a large scale aerial map (e.g., a high-resolution satellite image). State-of-the-art deep-learning based methods tackle this problem as deep metric learning which aims to learn global feature representations of the scene seen by the two different views. Despite promising results are obtained by such deep metric learning methods, they, however, fail to exploit a crucial cue relevant for localization, namely, the spatial layout of local features. Moreover, little attention is paid to the obvious domain gap (between aerial view and ground view) in the context of cross-view localization. This paper proposes a novel Cross-View Feature Transport (CVFT) technique to explicitly establish cross-view domain transfer that facilitates feature alignment between ground and aerial images. Specifically, we implement the CVFT as a network layer, which transports features from one domain to the other, leading to more meaningful feature similarity comparison. Our model is differentiable and can be learned end-to-end. Experiments on large-scale datasets have demonstrated that our method has remarkably boosted the state-of-the-art cross-view localization performance, e.g., on the CVUSA dataset, with significant improvements for top-1 recall from 40.79% to 61.43%, and for top-10 from 76.36% to 90.49%, compared with the previous state of the art [14]. We expect the key insight of the paper (i.e., explicitly handling domain difference via domain transport) will prove to be useful for other similar problems in computer vision as well.

Includes members:
  Member "lending orientation to neural networks for cross view geo localization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "university 1652 a multi view multi source benchmark for drone based geo localization")
  Member "feature selection for unsupervised domain adaptation using optimal transport":
    also connected through other hyperedges:
      (none)
  Member "deeppermnet visual permutation learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "instance separation emerges from inpainting")
  Member "localizing and orienting street views using overhead imagery":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multi channel attention selection gans for guided image to image translation")
  Member "semantic image based geolocation given a map":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "lending orientation to neural networks for cross view geo localization")
  Member "predicting ground level scene layout from aerial imagery":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multi channel attention selection gans for guided image to image translation")
  Member "semantic cross view matching":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "evaluation of cross view matching to improve ground vehicle localization with aerial perception")
  Member "optimal transport for domain adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "unsupervised multi class domain adaptation theory algorithms and practice")

Question: Please tell me which class the source paper 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)