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 "hybrid attention based decoupled metric learning for zero shot image retrieval"):
  Abstract: In zero-shot image retrieval (ZSIR) task, embedding learning becomes more attractive, however, many methods follow the traditional metric learning idea and omit the problems behind zero-shot settings. In this paper, we first emphasize the importance of learning visual discriminative metric and preventing the partial/selective learning behavior of learner in ZSIR, and then propose the Decoupled Metric Learning (DeML) framework to achieve these individually. Instead of coarsely optimizing an unified metric, we decouple it into multiple attention-specific parts so as to recurrently induce the discrimination and explicitly enhance the generalization. And they are mainly achieved by our object-attention module based on random walk graph propagation and the channel-attention module based on the adversary constraint, respectively. We demonstrate the necessity of addressing the vital problems in ZSIR on the popular benchmarks, outperforming the state-of-theart methods by a significant margin. Code is available at this http URL

Includes members:
  Member "energy confused adversarial metric learning for zero shot image retrieval and clustering":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "mixed high order attention network for person re identification")
  Member "signal to noise ratio a robust distance metric for deep metric learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "revisiting training strategies and generalization performance in deep metric learning")
  Member "deep metric learning with bier boosting independent embeddings robustly":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "revisiting training strategies and generalization performance in deep metric learning")
  Member "zero shot sketch image hashing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "sketch less for more on the fly fine grained sketch based image retrieval")
  Member "discriminative learning of latent features for zero shot recognition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "an optimal transport framework for zero shot learning")
  Member "almn deep embedding learning with geometrical virtual point generating":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "energy confused adversarial metric learning for zero shot image retrieval and clustering")
  Member "attention based ensemble for deep metric learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "revisiting training strategies and generalization performance in deep metric learning")
  Member "sampling matters in deep embedding learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "revisiting training strategies and generalization performance in deep metric 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)