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 online video stabilization"
  Abstract: Video stabilization technique is essential for most hand-held captured videos due to high-frequency shakes. Several 2D-, 2.5D- and 3D-based stabilization techniques are well studied, but to our knowledge, no solutions based on deep neural networks had been proposed. The reason for this is mostly the shortage of training data, as well as the challenge of modeling the problem using neural networks. In this paper, we solve the video stabilization problem using a convolutional neural network (ConvNet). Instead of dealing with offline holistic camera path smoothing based on feature matching, we focus on low-latency real-time camera path smoothing without explicitly representing the camera path. Our network, called StabNet, learns a transformation for each input unsteady frame progressively along the time-line, while creating a more stable latent camera path. To train the network, we create a dataset of synchronized steady/unsteady video pairs via a well designed hand-held hardware. Experimental results shows that the proposed online method (without using future frames) performs comparatively to traditional offline video stabilization methods, while running about 30 times faster. Further, the proposed StabNet is able to handle night-time and blurry videos, where existing methods fail in robust feature matching.

Connected through hyperedge HE-1 (induced by source paper "beyond the camera neural networks in world coordinates"):
  with members:
    - "learning to zoom a saliency based sampling layer for neural networks"
    - "slowfast networks for video recognition"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "non local neural networks"
    - "what actions are needed for understanding human actions in videos"
    - "deformable convolutional networks"
    - "sfm net learning of structure and motion from video"
    - "dynamic computational time for visual attention"

Connected through hyperedge HE-2 (induced by source paper "cinefilter unsupervised filtering for real time autonomous camera systems"):
  with members:
    - "watch to edit video retargeting using gaze"
    - "long term visual object tracking benchmark"
    - "learning multi domain convolutional neural networks for visual tracking"

Connected through hyperedge HE-3 (induced by source paper "stablenet semi online multi scale deep video stabilization"):
  with members:
    - "boundary flow a siamese network that predicts boundary motion without training on motion"
    - "mocogan decomposing motion and content for video generation"
    - "adam a method for stochastic optimization"

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.GR(Graphics)