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 "deep discrete hashing with self supervised pairwise labels"):
  Abstract: Hashing methods have been widely used for applications of large-scale image retrieval and classification. Non-deep hashing methods using handcrafted features have been significantly outperformed by deep hashing methods due to their better feature representation and end-to-end learning framework. However, the most striking successes in deep hashing have mostly involved discriminative models, which require labels. In this paper, we propose a novel unsupervised deep hashing method, named Deep Discrete Hashing (DDH), for large-scale image retrieval and classification. In the proposed framework, we address two main problems: 1) how to directly learn discrete binary codes? 2) how to equip the binary representation with the ability of accurate image retrieval and classification in an unsupervised way? We resolve these problems by introducing an intermediate variable and a loss function steering the learning process, which is based on the neighborhood structure in the original space. Experimental results on standard datasets (CIFAR-10, NUS-WIDE, and Oxford-17) demonstrate that our DDH significantly outperforms existing hashing methods by large margin in terms of~mAP for image retrieval and object recognition. Code is available at \url{this https URL}.

Includes members:
  Member "supervised learning of semantics preserving hash via deep convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "random vlad based deep hashing for efficient image retrieval")
  Member "hashnet deep learning to hash by continuation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a survey on deep hashing methods")
  Member "resnet in resnet generalizing residual architectures":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "convolutional networks with dense connectivity")
  Member "inception v4 inception resnet and the impact of residual connections on learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "stem seg spatio temporal embeddings for instance segmentation in videos")
  Member "a survey on learning to hash":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a survey on deep hashing methods")
  Member "sca cnn spatial and channel wise attention in convolutional networks for image captioning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "ha ccn hierarchical attention based crowd counting network")
  Member "learning to hash with binary deep neural network":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a survey on deep hashing methods")
  Member "salient object detection via augmented hypotheses":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "bridging saliency detection to weakly supervised object detection based on self paced curriculum learning")

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)