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 "generic sketch based retrieval learned without drawing a single sketch"):
  Abstract: We cast the sketch-based retrieval as edge-map matching. A shared convolutional network is trained to extract descriptors from edge maps and sketches, which are treated as a special case of edge maps. The network is fine-tuned solely from edge maps of landmark images. The training images are acquired in a fully unsupervised manner from 3D landmark models obtained by an automated structure-from-motion pipeline. #R##N#The proposed method achieves the state-of-the-art results on a standard benchmark. On two other fine-grained sketch-based retrieval benchmarks, it performs on par with or comes just after the method specifically designed for the dataset.

Includes members:
  Member "asymmetric feature maps with application to sketch based retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep semantic parsing of freehand sketches with homogeneous transformation soft weighted loss and staged learning")
  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 "deep sketch hashing fast free hand sketch based image retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a network pruning network approach to deep model compression")
  Member "deep image retrieval learning global representations for image search":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "constrained dominant sets and its applications in computer vision")
  Member "generalisation and sharing in triplet convnets for sketch based visual search":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "sketchformer transformer based representation for sketched structure")
  Member "efficient diffusion on region manifolds recovering small objects with compact cnn representations":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "constrained dominant sets and its applications in computer vision")
  Member "sketch based 3d shape retrieval using convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "sketch less for more on the fly fine grained sketch based image retrieval")
  Member "netvlad cnn architecture for weakly supervised place recognition":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning representations by predicting bags of visual words")

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)