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: "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.

Connected through hyperedge HE-1 (induced by source paper "exact adversarial attack to image captioning via structured output learning with latent variables"):
  with members:
    - "tencent ml images a large scale multi label image database for visual representation learning"
    - "target aware deep tracking"
    - "physical adversarial examples for object detectors"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "no need to worry about adversarial examples in object detection in autonomous vehicles"
    - "attacking visual language grounding with adversarial examples a case study on neural image captioning"
    - "on the robustness of semantic segmentation models to adversarial attacks"
    - "adversarial examples for semantic segmentation and object detection"

Connected through hyperedge HE-2 (induced by source paper "diversity in machine learning"):
  with members:
    - "fast determinantal point processes via distortion free intermediate sampling"
    - "improving sequential determinantal point processes for supervised video summarization"
    - "diversity promoting bayesian learning of latent variable models"
    - "learning detection with diverse proposals"
    - "diversified texture synthesis with feed forward networks"
    - "stochastic multiple choice learning for training diverse deep ensembles"
    - "an ensemble diversity approach to supervised binary hashing"
    - "mutual information and diverse decoding improve neural machine translation"

Connected through hyperedge HE-3 (induced by source paper "user aware folk popularity rank user popularity based tag recommendation that can enhance social popularity"):
  with members:
    - "stacked cross attention for image text matching"
    - "a multimodal approach to predict social media popularity"
    - "pay attention to virality understanding popularity of social media videos with the attention mechanism"
    - "modeling popularity in asynchronous social media streams with recurrent neural networks"
    - "neural collaborative filtering"
    - "use of deep learning in modern recommendation system a summary of recent works"
    - "stack captioning coarse to fine learning for image captioning"
    - "fast matrix factorization for online recommendation with implicit feedback"

Connected through hyperedge HE-4 (induced by source paper "multi label learning with missing labels using mixed dependency graphs"):
  with members:
    - "return of the devil in the details delving deep into convolutional nets"
    - "large scale multi label learning with missing labels"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-5 (induced by source paper "learning to generate time lapse videos using multi stage dynamic generative adversarial networks"):
  with members:
    - "decomposing motion and content for natural video sequence prediction"
    - "learning to generate long term future via hierarchical prediction"
    - "two stream convolutional networks for dynamic texture synthesis"
    - "perceptual generative adversarial networks for small object detection"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "mocogan decomposing motion and content for video generation"
    - "synthesising dynamic textures using convolutional neural networks"
    - "wasserstein learning of deep generative point process models"

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)