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: "fully automatic video colorization with self regularization and diversity"
  Abstract: We present a fully automatic approach to video colorization with self-regularization and diversity. Our model contains a colorization network for video frame colorization and a refinement network for spatiotemporal color refinement. Without any labeled data, both networks can be trained with self-regularized losses defined in bilateral and temporal space. The bilateral loss enforces color consistency between neighboring pixels in a bilateral space and the temporal loss imposes constraints between corresponding pixels in two nearby frames. While video colorization is a multi-modal problem, our method uses a perceptual loss with diversity to differentiate various modes in the solution space. Perceptual experiments demonstrate that our approach outperforms state-of-the-art approaches on fully automatic video colorization. The results are shown in the supplementary video at this https URL

Connected through hyperedge HE-1 (induced by source paper "cross modal learning for multi modal video categorization"):
  with members:
    - "learning relationships between text audio and video via deep canonical correlation for multimodal language analysis"
    - "bert for large scale video segment classification with test time augmentation"
    - "multimodal fusion with deep neural networks for audio video emotion recognition"
    - "vilbert pretraining task agnostic visiolinguistic representations for vision and language tasks"
    - "making sense of vision and touch learning multimodal representations for contact rich tasks"
    - "videobert a joint model for video and language representation learning"
    - "multimodal affective analysis using hierarchical attention strategy with word level alignment"
    - "cooperative learning of audio and video models from self supervised synchronization"

Connected through hyperedge HE-2 (induced by source paper "exploiting temporal coherence for multi modal video categorization"):
  with members:
    - "multimodal fusion with deep neural networks for audio video emotion recognition"
    - "making better mistakes leveraging class hierarchies with deep networks"
    - "vilbert pretraining task agnostic visiolinguistic representations for vision and language tasks"
    - "making sense of vision and touch learning multimodal representations for contact rich tasks"
    - "attention is all you need"
    - "youtube 8m a large scale video classification benchmark"
    - "unsupervised learning of visual representations using videos"
    - "netvlad cnn architecture for weakly supervised place recognition"

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)