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 "large scale open set deep logo detection"):
  Abstract: We present an open-set logo detection (OSLD) system, which can detect (localize and recognize) any number of unseen logo classes without re-training; it only requires a small set of canonical logo images for each logo class. We achieve this using a two-stage approach: (1) Generic logo detection to detect candidate logo regions in an image. (2) Logo matching for matching the detected logo regions to a set of canonical logo images to recognize them. We also introduce a 'simple deep metric learning' (SDML) framework that outperformed more complicated ensemble and attention models and boosted the logo matching accuracy. Furthermore, we constructed a new open-set logo detection dataset with thousands of logo classes, and will release it for research purposes. We demonstrate the effectiveness of OSLD on our dataset and on the standard Flickr-32 logo dataset, outperforming the state-of-the-art open-set and closed-set logo detection methods by a large margin.

Includes members:
  Member "combination of multiple global descriptors for image retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a benchmark on tricks for large scale image retrieval")
  Member "semantic granularity metric learning for visual search":
    also connected through other hyperedges:
      (none)
  Member "deep metric learning with bier boosting independent embeddings robustly":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "revisiting training strategies and generalization performance in deep metric learning")
  Member "deep face recognition a survey":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "relational deep feature learning for heterogeneous face recognition")
  Member "attention based ensemble for deep metric learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "revisiting training strategies and generalization performance in deep metric learning")
  Member "scalable logo recognition using proxies":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "unbiased evaluation of deep metric learning algorithms")
  Member "deep learning for logo recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a deep one shot network for query based logo retrieval")
  Member "convolutional neural network architecture for geometric matching":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "os2d one stage one shot object detection by matching anchor features")

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)