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 "tagging like humans diverse and distinct image annotation"):
  Abstract: In this work we propose a new automatic image annotation model, dubbed {\bf diverse and distinct image annotation} (D2IA). The generative model D2IA is inspired by the ensemble of human annotations, which create semantically relevant, yet distinct and diverse tags. In D2IA, we generate a relevant and distinct tag subset, in which the tags are relevant to the image contents and semantically distinct to each other, using sequential sampling from a determinantal point process (DPP) model. Multiple such tag subsets that cover diverse semantic aspects or diverse semantic levels of the image contents are generated by randomly perturbing the DPP sampling process. We leverage a generative adversarial network (GAN) model to train D2IA. Extensive experiments including quantitative and qualitative comparisons, as well as human subject studies, on two benchmark datasets demonstrate that the proposed model can produce more diverse and distinct tags than the state-of-the-arts.

Includes members:
  Member "multi label learning with missing labels using mixed dependency graphs":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "adversarial encoder multi task decoder for multi stage processes")
  Member "joint intermodal and intramodal label transfers for extremely rare or unseen classes":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "adaptive locality preserving regression")
  Member "multi label image recognition by recurrently discovering attentional regions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "pedestrian attribute recognition a survey")
  Member "learning spatial regularization with image level supervisions for multi label image classification":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "training object detectors from few weakly labeled and many unlabeled images")
  Member "learning to generate time lapse videos using multi stage dynamic generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "transformation based adversarial video prediction on large scale data")
  Member "improving pairwise ranking for multi label image classification":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "top 1 solution of multi moments in time challenge 2019")
  Member "annotation order matters recurrent image annotator for arbitrary length image tagging":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "orderless recurrent models for multi label classification")
  Member "semantic regularisation for recurrent image annotation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "improved hard example mining by discovering attribute based hard person identity")

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)