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 "asymmetric deep semantic quantization for image retrieval"):
  Abstract: Due to its fast retrieval and storage efficiency capabilities, hashing has been widely used in nearest neighbor retrieval tasks. By using deep learning-based techniques, hashing can outperform non-learning-based hashing technique in many applications. However, we argue that the current deep learning-based hashing methods ignore some critical problems (e.g., the learned hash codes are not discriminative due to the hashing methods being unable to discover rich semantic information and the training strategy having difficulty optimizing the discrete binary codes). In this paper, we propose a novel image hashing method, termed as asymmetric deep semantic quantization (ADSQ). The ADSQ is implemented using three stream frameworks, which consist of one LabelNet and two ImgNets. The LabelNet leverages the power of three fully-connected layers, which are used to capture rich semantic information between image pairs. For the two ImgNets, they each adopt the same convolutional neural network structure but with different weights (i.e., asymmetric convolutional neural networks). The two ImgNets are used to generate discriminative compact hash codes. Specifically, the function of the LabelNet is to capture rich semantic information that is used to guide the two ImgNets in minimizing the gap between the real-continuous features and the discrete binary codes. Furthermore, the ADSQ can utilize the most critical semantic information to guide the feature learning process and consider the consistency of the common semantic space and Hamming space. The experimental results on three benchmarks (i.e., CIFAR-10, NUS-WIDE, and ImageNet) demonstrate that the proposed ADSQ can outperform current state-of-the-art methods.

Includes members:
  Member "triplet based deep hashing network for cross modal retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "university 1652 a multi view multi source benchmark for drone based geo localization")
  Member "deep attention guided hashing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a survey on deep hashing methods")
  Member "deep ordinal hashing with spatial attention":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a survey on deep hashing methods")
  Member "deep priority hashing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a survey on deep hashing methods")
  Member "deep discrete supervised hashing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a survey on deep hashing methods")
  Member "deep supervised discrete hashing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a survey on deep hashing methods")
  Member "asymmetric deep supervised hashing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a survey on deep hashing methods")
  Member "a survey on learning to hash":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a survey on deep hashing methods")

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)