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 "transductive zero shot hashing for multi label image retrieval"):
  Abstract: Hash coding has been widely used in approximate nearest neighbor search for large-scale image retrieval. Given semantic annotations such as class labels and pairwise similarities of the training data, hashing methods can learn and generate effective and compact binary codes. While some newly introduced images may contain undefined semantic labels, which we call unseen images, zeor-shot hashing techniques have been studied. However, existing zeor-shot hashing methods focus on the retrieval of single-label images, and cannot handle multi-label images. In this paper, for the first time, a novel transductive zero-shot hashing method is proposed for multi-label unseen image retrieval. In order to predict the labels of the unseen/target data, a visual-semantic bridge is built via instance-concept coherence ranking on the seen/source data. Then, pairwise similarity loss and focal quantization loss are constructed for training a hashing model using both the seen/source and unseen/target data. Extensive evaluations on three popular multi-label datasets demonstrate that, the proposed hashing method achieves significantly better results than the competing methods.

Includes members:
  Member "exploring auxiliary context discrete semantic transfer hashing for scalable image retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "locality preserving joint transfer for domain adaptation")
  Member "supervised discrete hashing with relaxation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep hashing learning for visual and semantic retrieval of remote sensing images")
  Member "improved deep hashing with soft pairwise similarity for multi label image retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "comprehensive and efficient data labeling via adaptive model scheduling")
  Member "deep hashing network for unsupervised domain adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "revisiting meta learning as supervised learning")
  Member "hashing in the zero shot framework with domain adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "transductive zero shot hashing via coarse to fine similarity mining")
  Member "hashnet deep learning to hash by continuation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a survey on deep hashing methods")
  Member "scalable image retrieval by sparse product quantization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "generalized product quantization network for semi supervised image retrieval")
  Member "compact hash codes for efficient visual descriptors retrieval in large scale databases":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "binary constrained deep hashing network for image retrieval without human intervention")

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)