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: "persistent homology of attractors for action recognition"
  Abstract: In this paper, we propose a novel framework for dynamical analysis of human actions from 3D motion capture data using topological data analysis. We model human actions using the topological features of the attractor of the dynamical system. We reconstruct the phase-space of time series corresponding to actions using time-delay embedding, and compute the persistent homology of the phase-space reconstruction. In order to better represent the topological properties of the phase-space, we incorporate the temporal adjacency information when computing the homology groups. The persistence of these homology groups encoded using persistence diagrams are used as features for the actions. Our experiments with action recognition using these features demonstrate that the proposed approach outperforms other baseline methods.

Connected through hyperedge HE-1 (induced by source paper "efficient topological layer based on persistent landscapes"):
  with members:
    - "a topology layer for machine learning"
    - "neural persistence a complexity measure for deep neural networks using algebraic topology"
    - "connectivity optimized representation learning via persistent homology"
    - "on characterizing the capacity of neural networks using algebraic topology"
    - "deep sets"
    - "topological approaches to deep learning"
    - "a persistence landscapes toolbox for topological statistics"
    - "deep learning with topological signatures"

Connected through hyperedge HE-2 (induced by source paper "deep learning of dynamical attractors from time series measurements"):
  with members:
    - "scipy 1 0 fundamental algorithms for scientific computing in python"
    - "learning low dimensional state embeddings and metastable clusters from time series data"
    - "learning koopman invariant subspaces for dynamic mode decomposition"
    - "reducing overfitting in deep networks by decorrelating representations"

Connected through hyperedge HE-3 (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"
    - "quasi periodicity quantification in video data using topology"
    - "deep learning with topological signatures"
    - "a taxonomy of deep convolutional neural nets for computer vision"
    - "dense optical flow prediction from a static image"

Connected through hyperedge HE-4 (induced by source paper "perturbation robust representations of topological persistence diagrams"):
  with members:
    - "quasi periodicity quantification in video data using topology"
    - "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"
    - "geodesic convolutional neural networks on riemannian manifolds"
    - "expanding the family of grassmannian kernels an embedding perspective"

Connected through hyperedge HE-5 (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"
    - "quasi periodicity quantification in video data using topology"
    - "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-6 (induced by source paper "topological eulerian synthesis of slow motion periodic videos"):
  with members:
    - "quasi periodicity quantification in video data using topology"
    - "shape distributions of nonlinear dynamical systems for video based inference"

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.CG(Computational Geometry)