human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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

Connected through hyperedge HE-1 (induced by source paper "mixed high order attention network for person re identification"):
  with members:
    - "energy confused adversarial metric learning for zero shot image retrieval and clustering"
    - "multi level factorisation net for person re identification"
    - "end to end deep kronecker product matching for person re identification"
    - "harmonious attention network for person re identification"
    - "re identification with consistent attentive siamese networks"
    - "attention aware compositional network for person re identification"
    - "discriminative learning of latent features for zero shot recognition"
    - "human semantic parsing for person re identification"

Connected through hyperedge HE-2 (induced by source paper "learning similarity attention"):
  with members:
    - "multi similarity loss with general pair weighting for deep metric learning"
    - "hardness aware deep metric learning"
    - "learning discriminative features with multiple granularities for person re identification"
    - "dual attention matching network for context aware feature sequence based person re identification"
    - "attention based ensemble for deep metric learning"
    - "tell me where to look guided attention inference network"
    - "sampling matters in deep embedding learning"
    - "attention is all you need"

Connected through hyperedge HE-3 (induced by source paper "a simple and effective framework for pairwise deep metric learning"):
  with members:
    - "multi similarity loss with general pair weighting for deep metric learning"
    - "revisiting metric learning for few shot image classification"
    - "deep metric learning with bier boosting independent embeddings robustly"
    - "large scale distance metric learning with uncertainty"
    - "hard aware point to set deep metric for person re identification"
    - "deep metric learning with hierarchical triplet loss"
    - "attention based ensemble for deep metric learning"
    - "sampling matters in deep embedding learning"

Connected through hyperedge HE-4 (induced by source paper "signal to noise ratio a robust distance metric for deep metric learning"):
  with members:
    - "energy confused adversarial metric learning for zero shot image retrieval and clustering"
    - "sketchmate deep hashing for million scale human sketch retrieval"
    - "almn deep embedding learning with geometrical virtual point generating"
    - "deep hashing network for unsupervised domain adaptation"
    - "deep metric learning with angular loss"
    - "deep sketch hashing fast free hand sketch based image retrieval"
    - "deep supervised hashing with triplet labels"
    - "training region based object detectors with online hard example mining"

Question: Please tell me which class the center node 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)