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 "university 1652 a multi view multi source benchmark for drone based geo localization"):
  Abstract: We consider the problem of cross-view geo-localization. The primary challenge of this task is to learn the robust feature against large viewpoint changes. Existing benchmarks can help, but are limited in the number of viewpoints. Image pairs, containing two viewpoints, e.g., satellite and ground, are usually provided, which may compromise the feature learning. Besides phone cameras and satellites, in this paper, we argue that drones could serve as the third platform to deal with the geo-localization problem. In contrast to the traditional ground-view images, drone-view images meet fewer obstacles, e.g., trees, and could provide a comprehensive view when flying around the target place. To verify the effectiveness of the drone platform, we introduce a new multi-view multi-source benchmark for drone-based geo-localization, named University-1652. University-1652 contains data from three platforms, i.e., synthetic drones, satellites and ground cameras of 1,652 university buildings around the world. To our knowledge, University-1652 is the first drone-based geo-localization dataset and enables two new tasks, i.e., drone-view target localization and drone navigation. As the name implies, drone-view target localization intends to predict the location of the target place via drone-view images. On the other hand, given a satellite-view query image, drone navigation is to drive the drone to the area of interest in the query. We use this dataset to analyze a variety of off-the-shelf CNN features and propose a strong CNN baseline on this challenging dataset. The experiments show that University-1652 helps the model to learn the viewpoint-invariant features and also has good generalization ability in the real-world scenario.

Includes members:
  Member "building information modeling and classification by visual learning at a city scale":
    also connected through other hyperedges:
      (none)
  Member "triplet based deep hashing network for cross modal retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "revisiting embodiedqa a simple baseline and beyond")
  Member "lending orientation to neural networks for cross view geo localization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "evaluation of cross view matching to improve ground vehicle localization with aerial perception")
  Member "optimal feature transport for cross view image geo localization":
    also connected through other hyperedges:
      (none)
  Member "vision meets drones a challenge":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "panda a gigapixel level human centric video dataset")
  Member "stochastic attraction repulsion embedding for large scale image localization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the apolloscape open dataset for autonomous driving and its application")
  Member "dcan dual channel wise alignment networks for unsupervised scene adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "madan multi source adversarial domain aggregation network for domain adaptation")
  Member "revisiting oxford and paris large scale image retrieval benchmarking":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "unifying deep local and global features for image search")

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)