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: "video acceleration magnification"
  Abstract: The ability to amplify or reduce subtle image changes over time is useful in contexts such as video editing, medical video analysis, product quality control and sports. In these contexts there is often large motion present which severely distorts current video amplification methods that magnify change linearly. In this work we propose a method to cope with large motions while still magnifying small changes. We make the following two observations: i) large motions are linear on the temporal scale of the small changes; ii) small changes deviate from this linearity. We ignore linear motion and propose to magnify acceleration. Our method is pure Eulerian and does not require any optical flow, temporal alignment or region annotations. We link temporal second-order derivative filtering to spatial acceleration magnification. We apply our method to moving objects where we show motion magnification and color magnification. We provide quantitative as well as qualitative evidence for our method while comparing to the state-of-the-art.

Connected through hyperedge HE-1 (induced by source paper "a boost in revealing subtle facial expressions a consolidated eulerian framework"):
  with members:
    - "spatiotemporal recurrent convolutional networks for recognizing spontaneous micro expressions"
    - "cross database micro expression recognition a benchmark"
    - "towards reading hidden emotions a comparative study of spontaneous micro expression spotting and recognition methods"
    - "micro expression spotting a benchmark"
    - "reading hidden emotions spontaneous micro expression spotting and recognition"

Connected through hyperedge HE-2 (induced by source paper "hyedge optimal request scheduling in hybrid edge computing environment"):
  with members:
    - "a survey on mobile edge computing the communication perspective"
    - "dynamic service placement for mobile micro clouds with predicted future costs"
    - "delay optimal computation task scheduling for mobile edge computing systems"
    - "dynamic computation offloading for mobile edge computing with energy harvesting devices"
    - "efficient multi user computation offloading for mobile edge cloud computing"

Connected through hyperedge HE-3 (induced by source paper "efficient real time camera based estimation of heart rate and its variability"):
  with members:
    - "deepphys video based physiological measurement using convolutional attention networks"

Connected through hyperedge HE-4 (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"
    - "deep complex networks"
    - "hidden two stream convolutional networks for action recognition"
    - "the kinetics human action video dataset"
    - "actionflownet learning motion representation for action recognition"

Connected through hyperedge HE-5 (induced by source paper "learning based video motion magnification"):
  with members:
    - "network dissection quantifying interpretability of deep visual representations"
    - "personalized cinemagraphs using semantic understanding and collaborative learning"
    - "decomposing motion and content for natural video sequence prediction"
    - "video frame interpolation via adaptive convolution"
    - "light field video capture using a learning based hybrid imaging system"
    - "video frame interpolation via adaptive separable convolution"
    - "video frame synthesis using deep voxel flow"
    - "perceptual losses for real time style transfer and super resolution"

Connected through hyperedge HE-6 (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"
    - "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"
    - "making a case for learning motion representations with phase"
    - "deepcut joint subset partition and labeling for multi person pose estimation"

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)