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 "traversing the continuous spectrum of image retrieval with deep dynamic models"):
  Abstract: We introduce the first work to tackle the image retrieval problem as a continuous operation. While the proposed approaches in the literature can be roughly categorized into two main groups: category- and instance-based retrieval, in this work we show that the retrieval task is much richer and more complex. Image similarity goes beyond this discrete vantage point and spans a continuous spectrum among the classical operating points of category and instance similarity. However, current retrieval models are static and incapable of exploring this rich structure of the retrieval space since they are trained and evaluated with a single operating point as a target objective. Hence, we introduce a novel retrieval model that for a given query is capable of producing a dynamic embedding that can target an arbitrary point along the continuous retrieval spectrum. Our model disentangles the visual signal of a query image into its basic components of categorical and attribute information. Furthermore, using a continuous control parameter our model learns to reconstruct a dynamic embedding of the query by mixing these components with different proportions to target a specific point along the retrieval simplex. We demonstrate our idea in a comprehensive evaluation of the proposed model and highlight the advantages of our approach against a set of well-established discrete retrieval models.

Includes members:
  Member "self supervised adversarial hashing networks for cross modal retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a survey on deep hashing methods")
  Member "deep image retrieval learning global representations for image search":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "constrained dominant sets and its applications in computer vision")
  Member "key value memory networks for directly reading documents":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "attention over parameters for dialogue systems")
  Member "large scale image retrieval with attentive deep local features":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "image matching across wide baselines from paper to practice")
  Member "conditional similarity networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "fine grained fashion similarity learning by attribute specific embedding network")
  Member "dynamic memory networks for visual and textual question answering":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "component analysis for visual question answering architectures")
  Member "cnn image retrieval learns from bow unsupervised fine tuning with hard examples":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "image matching across wide baselines from paper to practice")
  Member "cross domain image retrieval with a dual attribute aware ranking network":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "fine grained fashion similarity learning by attribute specific embedding network")

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)