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: "making a case for learning motion representations with phase"
  Abstract: This work advocates Eulerian motion representation learning over the current standard Lagrangian optical flow model. Eulerian motion is well captured by using phase, as obtained by decomposing the image through a complex-steerable pyramid. We discuss the gain of Eulerian motion in a set of practical use cases: (i) action recognition, (ii) motion prediction in static images, (iii) motion transfer in static images and, (iv) motion transfer in video. For each task we motivate the phase-based direction and provide a possible approach.

Connected through hyperedge HE-1 (induced by source paper "using phase instead of optical flow for action recognition"):
  with members:
    - "phasenet for video frame interpolation"
    - "learning based video motion magnification"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "on the integration of optical flow and action recognition"
    - "video acceleration magnification"
    - "deep complex networks"
    - "hidden two stream convolutional networks for action recognition"
    - "the kinetics human action video dataset"

Connected through hyperedge HE-2 (induced by source paper "hand tremor frequency estimation in videos"):
  with members:
    - "real world repetition estimation by div grad and curl"
    - "parkinson s disease assessment from a wrist worn wearable sensor in free living conditions deep ensemble learning and visualization"
    - "video acceleration magnification"
    - "continuous video to simple signals for swimming stroke detection with convolutional neural networks"
    - "quasi periodicity quantification in video data using topology"
    - "convolutional pose machines"
    - "human pose estimation via convolutional part heatmap regression"
    - "deepcut joint subset partition and labeling for multi person pose estimation"

Connected through hyperedge HE-3 (induced by source paper "one step time dependent future video frame prediction with a convolutional encoder decoder neural network"):
  with members:
    - "learning to generate long term future via hierarchical prediction"
    - "perceptual losses for real time style transfer and super resolution"
    - "conditional image generation with pixelcnn decoders"
    - "generative adversarial text to image synthesis"
    - "artistic style transfer for videos"
    - "an uncertain future forecasting from static images using variational autoencoders"
    - "pixel recurrent neural networks"
    - "generating videos with scene dynamics"

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)