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 "benchmarking unsupervised near duplicate image detection"):
  Abstract: Abstract   Unsupervised near-duplicate detection has many practical applications ranging from social media analysis and web-scale retrieval, to digital image forensics. It entails running a threshold-limited query on a set of descriptors extracted from the images, with the goal of identifying all possible near-duplicates, while limiting the false positives due to visually similar images. Since the rate of false alarms grows with the dataset size, a very high specificity is thus required, up to 1–10       −  9      for realistic use cases; this important requirement, however, is often overlooked in literature. In recent years, descriptors based on deep convolutional neural networks have matched or surpassed traditional feature extraction methods in content-based image retrieval tasks. To the best of our knowledge, ours is the first attempt to establish the performance range of deep learning-based descriptors for unsupervised near-duplicate detection on a range of datasets, encompassing a broad spectrum of near-duplicate definitions. We leverage both established and new benchmarks, such as the Mir-Flick Near-Duplicate (MFND) dataset, in which a known ground truth is provided for all possible pairs over a general, large scale image collection. To compare the specificity of different descriptors, we reduce the problem of unsupervised detection to that of binary classification of near-duplicate vs. not-near-duplicate images. The latter can be conveniently characterized using Receiver Operating Curve (ROC). Our findings in general favor the choice of fine-tuning deep convolutional networks, as opposed to using off-the-shelf features, but differences at high specificity settings depend on the dataset and are often small. The best performance was observed on the MFND benchmark, achieving 96% sensitivity at a false positive rate of     1.43  ×   10   −  6      .

Includes members:
  Member "an anti fraud system for car insurance claim based on visual evidence":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep learning in the automotive industry recent advances and application examples")
  Member "billion scale similarity search with gpus":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "noise estimation using density estimation for self supervised multimodal learning")
  Member "recent advance in content based image retrieval a literature survey":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "machine learning based co creative design framework")
  Member "saliency weighted convolutional features for instance search":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deeply activated salient region for instance search")
  Member "end to end learning of deep visual representations for image retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-5 (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-6 (induced by source paper "constrained dominant sets and its applications in computer vision")
  Member "sift meets cnn a decade survey of instance retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "matching neuromorphic events and color images via adversarial learning")
  Member "cross dimensional weighting for aggregated deep convolutional features":
    also connected through other hyperedges:
      - Hyperedge HE-8 (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)