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: "real world repetition estimation by div grad and curl"
  Abstract: We consider the problem of estimating repetition in video, such as performing push-ups, cutting a melon or playing violin. Existing work shows good results under the assumption of static and stationary periodicity. As realistic video is rarely perfectly static and stationary, the often preferred Fourier-based measurements is inapt. Instead, we adopt the wavelet transform to better handle non-static and non-stationary video dynamics. From the flow field and its differentials, we derive three fundamental motion types and three motion continuities of intrinsic periodicity in 3D. On top of this, the 2D perception of 3D periodicity considers two extreme viewpoints. What follows are 18 fundamental cases of recurrent perception in 2D. In practice, to deal with the variety of repetitive appearance, our theory implies measuring time-varying flow and its differentials (gradient, divergence and curl) over segmented foreground motion. For experiments, we introduce the new QUVA Repetition dataset, reflecting reality by including non-static and non-stationary videos. On the task of counting repetitions in video, we obtain favorable results compared to a deep learning alternative.

Connected through hyperedge HE-1 (induced by source paper "energy based periodicity mining with deep features for action repetition counting in unconstrained videos"):
  with members:
    - "the kinetics human action video dataset"
    - "temporal segment networks towards good practices for deep action recognition"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "unsupervised temporal segmentation of repetitive human actions based on kinematic modeling and frequency analysis"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-2 (induced by source paper "reactnet temporal localization of repetitive activities in real world videos"):
  with members:
    - "hand tremor frequency estimation in videos"
    - "on loss functions for deep neural networks in classification"
    - "stacked hourglass networks for human pose estimation"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-3 (induced by source paper "unsupervised generation of optical flow datasets"):
  with members:
    - "learning human optical flow"
    - "what makes good synthetic training data for learning disparity and optical flow estimation"
    - "three for one and one for three flow segmentation and surface normals"
    - "liteflownet a lightweight convolutional neural network for optical flow estimation"
    - "accurate optical flow via direct cost volume processing"
    - "the 2017 davis challenge on video object segmentation"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"
    - "im2flow motion hallucination from static images for action recognition"

Connected through hyperedge HE-4 (induced by source paper "subitizing with variational autoencoders"):
  with members:
    - "spatiotemporal modeling for crowd counting in videos"
    - "representation learning by learning to count"
    - "deep feature consistent variational autoencoder"
    - "imbalanced learn a python toolbox to tackle the curse of imbalanced datasets in machine learning"
    - "counting everyday objects in everyday scenes"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "learning to count with deep object features"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-5 (induced by source paper "hand tremor frequency estimation in videos"):
  with members:
    - "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"
    - "making a case for learning motion representations with phase"
    - "deepcut joint subset partition and labeling for multi person pose estimation"

Connected through hyperedge HE-6 (induced by source paper "repetition estimation"):
  with members:
    - "structure from recurrent motion from rigidity to recurrency"
    - "quasi periodicity quantification in video data using topology"
    - "learning motion patterns in videos"
    - "a large dataset to train convolutional networks for disparity optical flow and scene flow estimation"
    - "epicflow edge preserving interpolation of correspondences for optical flow"

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)