human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "learning based video motion magnification"):
  Abstract: Video motion magnification techniques allow us to see small motions previously invisible to the naked eyes, such as those of vibrating airplane wings, or swaying buildings under the influence of the wind. Because the motion is small, the magnification results are prone to noise or excessive blurring. The state of the art relies on hand-designed filters to extract representations that may not be optimal. In this paper, we seek to learn the filters directly from examples using deep convolutional neural networks. To make training tractable, we carefully design a synthetic dataset that captures small motion well, and use two-frame input for training. We show that the learned filters achieve high-quality results on real videos, with less ringing artifacts and better noise characteristics than previous methods. While our model is not trained with temporal filters, we found that the temporal filters can be used with our extracted representations up to a moderate magnification, enabling a frequency-based motion selection. Finally, we analyze the learned filters and show that they behave similarly to the derivative filters used in previous works. Our code, trained model, and datasets will be available online.

Includes members:
  Member "network dissection quantifying interpretability of deep visual representations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "grad cam visual explanations from deep networks via gradient based localization")
  Member "personalized cinemagraphs using semantic understanding and collaborative learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "animating landscape self supervised learning of decoupled motion and appearance for single image video synthesis")
  Member "decomposing motion and content for natural video sequence prediction":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "transformation based adversarial video prediction on large scale data")
  Member "video acceleration magnification":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a boost in revealing subtle facial expressions a consolidated eulerian framework")
  Member "video frame interpolation via adaptive convolution":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "softmax splatting for video frame interpolation")
  Member "light field video capture using a learning based hybrid imaging system":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "synthetic defocus and look ahead autofocus for casual videography")
  Member "video frame interpolation via adaptive separable convolution":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "softmax splatting for video frame interpolation")
  Member "video frame synthesis using deep voxel flow":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "transformation based adversarial video prediction on large scale data")

Question: Please tell me which class the source paper 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)