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: "simple to complex cross modal learning to rank"
  Abstract: The heterogeneity-gap between different modalities brings a significant challenge to multimedia information retrieval. Some studies formalize the cross-modal retrieval tasks as a ranking problem and learn a shared multi-modal embedding space to measure the cross-modality similarity. However, previous methods often establish the shared embedding space based on linear mapping functions which might not be sophisticated enough to reveal more complicated inter-modal correspondences. Additionally, current studies assume that the rankings are of equal importance, and thus all rankings are used simultaneously, or a small number of rankings are selected randomly to train the embedding space at each iteration. Such strategies, however, always suffer from outliers as well as reduced generalization capability due to their lack of insightful understanding of procedure of human cognition. In this paper, we involve the self-paced learning theory with diversity into the cross-modal learning to rank and learn an optimal multi-modal embedding space based on non-linear mapping functions. This strategy enhances the model's robustness to outliers and achieves better generalization via training the model gradually from easy rankings by diverse queries to more complex ones. An efficient alternative algorithm is exploited to solve the proposed challenging problem with fast convergence in practice. Extensive experimental results on several benchmark datasets indicate that the proposed method achieves significant improvements over the state-of-the-arts in this literature.

Connected through hyperedge HE-1 (induced by source paper "object oriented video captioning with temporal graph and prior knowledge building"):
  with members:
    - "object aware aggregation with bidirectional temporal graph for video captioning"
    - "yolov3 an incremental improvement"
    - "reconstruction network for video captioning"
    - "exploit the connectivity multi object tracking with trackletnet"
    - "learning spatio temporal features with 3d residual networks for action recognition"
    - "dense captioning events in videos"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "mask r cnn"

Connected through hyperedge HE-2 (induced by source paper "multi view deep subspace clustering networks"):
  with members:
    - "deep clustering for unsupervised learning of visual features"
    - "deep multimodal subspace clustering networks"
    - "subspace clustering by block diagonal representation"
    - "non local neural networks"
    - "deep subspace clustering networks"
    - "deconvolutional paragraph representation learning"
    - "plug play generative networks conditional iterative generation of images in latent space"
    - "multi view 3d object detection network for autonomous driving"

Connected through hyperedge HE-3 (induced by source paper "multi modal coreference resolution with the correlation between space structures"):
  with members:
    - "multimodal retrieval with asymmetrically weighted truncated svd canonical correlation analysis"
    - "deep fragment embeddings for bidirectional image sentence mapping"

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.LG(Machine Learning)