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: "quasi periodicity quantification in video data using topology"
  Abstract: This work introduces a novel framework for quantifying the presence and strength of recurrent dynamics in video data. Specifically, we provide continuous measures of periodicity (perfect repetition) and quasiperiodicity (superposition of periodic modes with non-commensurate periods), in a way which does not require segmentation, training, object tracking or 1-dimensional surrogate signals. Our methodology operates directly on video data. The approach combines ideas from nonlinear time series analysis (delay embeddings) and computational topology (persistent homology), by translating the problem of finding recurrent dynamics in video data, into the problem of determining the circularity or toroidality of an associated geometric space. Through extensive testing, we show the robustness of our scores with respect to several noise models/levels, we show that our periodicity score is superior to other methods when compared to human-generated periodicity rankings, and furthermore, we show that our quasiperiodicity score clearly indicates the presence of biphonation in videos of vibrating vocal folds, which has never before been accomplished end to end quantitatively.

Connected through hyperedge HE-1 (induced by source paper "pi net a deep learning approach to extract topological persistence images"):
  with members:
    - "topology of learning in artificial neural networks"
    - "neural persistence a complexity measure for deep neural networks using algebraic topology"
    - "exposition and interpretation of the topology of neural networks"
    - "perturbation robust representations of topological persistence diagrams"
    - "deep learning with topological signatures"
    - "persistent homology of attractors for action recognition"
    - "a taxonomy of deep convolutional neural nets for computer vision"
    - "dense optical flow prediction from a static image"

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"
    - "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-3 (induced by source paper "perturbation robust representations of topological persistence diagrams"):
  with members:
    - "deep learning with topological signatures"
    - "learning a deep model for human action recognition from novel viewpoints"
    - "syncspeccnn synchronized spectral cnn for 3d shape segmentation"
    - "geometric deep learning on graphs and manifolds using mixture model cnns"
    - "geometry helps to compare persistence diagrams"
    - "persistent homology of attractors for action recognition"
    - "geodesic convolutional neural networks on riemannian manifolds"
    - "expanding the family of grassmannian kernels an embedding perspective"

Connected through hyperedge HE-4 (induced by source paper "time series featurization via topological data analysis"):
  with members:
    - "deep learning for time series classification a review"
    - "on characterizing the capacity of neural networks using algebraic topology"
    - "a short survey of topological data analysis in time series and systems analysis"
    - "persistent homology of attractors for action recognition"
    - "introduction to the r package tda"
    - "an introductory study on time series modeling and forecasting"
    - "towards persistence based reconstruction in euclidean spaces"

Connected through hyperedge HE-5 (induced by source paper "repetition estimation"):
  with members:
    - "real world repetition estimation by div grad and curl"
    - "structure from recurrent motion from rigidity to recurrency"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "computing wasserstein distance for persistence diagrams on a quantum computer"):
  with members:
    - "geometry helps to compare persistence diagrams"
    - "the theory of the interleaving distance on multidimensional persistence modules"

Connected through hyperedge HE-7 (induced by source paper "topological eulerian synthesis of slow motion periodic videos"):
  with members:
    - "shape distributions of nonlinear dynamical systems for video based inference"
    - "persistent homology of attractors for action recognition"

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)