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 policy hashing network with listwise supervision"):
  Abstract: Deep-networks-based hashing has become a leading approach for large-scale image retrieval, which learns a similarity-preserving network to map similar images to nearby hash codes. The pairwise and triplet losses are two widely used similarity preserving manners for deep hashing. These manners ignore the fact that hashing is a prediction task on the list of binary codes. However, learning deep hashing with listwise supervision is challenging in 1) how to obtain the rank list of whole training set when the batch size of the deep network is always small and 2) how to utilize the listwise supervision. In this paper, we present a novel deep policy hashing architecture with two systems are learned in parallel: a query network and a shared and slowly changing database network. The following three steps are repeated until convergence: 1) the database network encodes all training samples into binary codes to obtain a whole rank list, 2) the query network is trained based on policy learning to maximize a reward that indicates the performance of the whole ranking list of binary codes, e.g., mean average precision (MAP), and 3) the database network is updated as the query network. Extensive evaluations on several benchmark datasets show that the proposed method brings substantial improvements over state-of-the-art hashing methods.

Includes members:
  Member "fast supervised discrete hashing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "asymmetric correlation quantization hashing for cross modal retrieval")
  Member "supervised learning of semantics preserving hash via deep convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "random vlad based deep hashing for efficient image retrieval")
  Member "deep reinforcement learning for image hashing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "simultaneous region localization and hash coding for fine grained image retrieval")
  Member "blockdrop dynamic inference paths in residual networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "switchable precision neural networks")
  Member "hashnet deep learning to hash by continuation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a survey on deep hashing methods")
  Member "fast training of triplet based deep binary embedding networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep heterogeneous hashing for face video retrieval")
  Member "a survey on learning to hash":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a survey on deep hashing methods")
  Member "asynchronous methods for deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deep reinforcement learning for autonomous driving a survey")

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)