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 "stochastic attraction repulsion embedding for large scale image localization"):
  Abstract: This paper tackles the problem of large-scale image-based localization (IBL) where the spatial location of a query image is determined by finding out the most similar reference images in a large database. For solving this problem, a critical task is to learn discriminative image representation that captures informative information relevant for localization. We propose a novel representation learning method having higher location-discriminating power. It provides the following contributions: 1) we represent a place (location) as a set of exemplar images depicting the same landmarks and aim to maximize similarities among intra-place images while minimizing similarities among inter-place images; 2) we model a similarity measure as a probability distribution on L_2-metric distances between intra-place and inter-place image representations; 3) we propose a new Stochastic Attraction and Repulsion Embedding (SARE) loss function minimizing the KL divergence between the learned and the actual probability distributions; 4) we give theoretical comparisons between SARE, triplet ranking and contrastive losses. It provides insights into why SARE is better by analyzing gradients. Our SARE loss is easy to implement and pluggable to any CNN. Experiments show that our proposed method improves the localization performance on standard benchmarks by a large margin. Demonstrating the broad applicability of our method, we obtained the third place out of 209 teams in the 2018 Google Landmark Retrieval Challenge. Our code and model are available at this https URL.

Includes members:
  Member "cplanet enhancing image geolocalization by combinatorial partitioning of maps":
    also connected through other hyperedges:
      (none)
  Member "sampling matters in deep embedding learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "revisiting training strategies and generalization performance in deep metric learning")
  Member "depth map completion by jointly exploiting blurry color images and sparse depth maps":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "unsupervised enhancement of real world depth images using tri cycle gan")
  Member "benchmarking 6dof urban visual localization in changing conditions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "real time kinematic ground truth for the oxford robotcar dataset")
  Member "deep metric learning with angular loss":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "revisiting training strategies and generalization performance in deep metric learning")
  Member "revisiting im2gps in the deep learning era":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "clusterfit improving generalization of visual representations")
  Member "end to end learning of deep visual representations for image retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "mvp unified motion and visual self supervised learning for large scale robotic navigation")
  Member "deep image retrieval learning global representations for image search":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "constrained dominant sets and its applications in computer vision")

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)