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: "deep video color propagation"
  Abstract: Traditional approaches for color propagation in videos rely on some form of matching between consecutive video frames. Using appearance descriptors, colors are then propagated both spatially and temporally. These methods, however, are computationally expensive and do not take advantage of semantic information of the scene. In this work we propose a deep learning framework for color propagation that combines a local strategy, to propagate colors frame-by-frame ensuring temporal stability, and a global strategy, using semantics for color propagation within a longer range. Our evaluation shows the superiority of our strategy over existing video and image color propagation methods as well as neural photo-realistic style transfer approaches.

Connected through hyperedge HE-1 (induced by source paper "softmax splatting for video frame interpolation"):
  with members:
    - "unsupervised video interpolation using cycle consistency"
    - "depth aware video frame interpolation"
    - "video enhancement with task oriented flow"
    - "phasenet for video frame interpolation"
    - "temporal interpolation as an unsupervised pretraining task for optical flow estimation"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "learning to synthesize motion blur"
    - "geometry aware deep network for single image novel view synthesis"

Connected through hyperedge HE-2 (induced by source paper "deep exemplar based video colorization"):
  with members:
    - "the relativistic discriminator a key element missing from standard gan"
    - "learning blind video temporal consistency"
    - "spectral normalization for generative adversarial networks"
    - "switchable temporal propagation network"
    - "deep exemplar based colorization"
    - "the contextual loss for image transformation with non aligned data"
    - "pixel level semantics guided image colorization"
    - "tracking emerges by colorizing videos"

Connected through hyperedge HE-3 (induced by source paper "fully automatic video colorization with self regularization and diversity"):
  with members:
    - "learning blind video temporal consistency"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "switchable temporal propagation network"
    - "deep exemplar based colorization"
    - "tracking emerges by colorizing videos"
    - "colorization as a proxy task for visual understanding"
    - "real time user guided image colorization with learned deep priors"
    - "learning representations for automatic colorization"

Connected through hyperedge HE-4 (induced by source paper "automatic video colorization using 3d conditional generative adversarial networks"):
  with members:
    - "training gans with optimism"
    - "improved techniques for training gans"
    - "image to image translation with conditional adversarial networks"
    - "colorful image colorization"
    - "u net convolutional networks for biomedical image segmentation"
    - "approximate nearest neighbor fields in video"

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)