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: "compressive acquisition of dynamic scenes"
  Abstract: Compressive sensing (CS) is a new approach for the acquisition and recovery of sparse signals and images that enables sampling rates significantly below the classical Nyquist rate. Despite significant progress in the theory and methods of CS, little headway has been made in compressive video acquisition and recovery. Video CS is complicated by the ephemeral nature of dynamic events, which makes direct extensions of standard CS imaging architectures and signal models difficult. In this paper, we develop a new framework for video CS for dynamic textured scenes that models the evolution of the scene as a linear dynamical system (LDS). This reduces the video recovery problem to first estimating the model parameters of the LDS from compressive measurements, and then reconstructing the image frames. We exploit the low-dimensional dynamic parameters (the state sequence) and high-dimensional static parameters (the observation matrix) of the LDS to devise a novel compressive measurement strategy that measures only the dynamic part of the scene at each instant and accumulates measurements over time to estimate the static parameters. This enables us to lower the compressive measurement rate considerably. We validate our approach with a range of experiments involving both video recovery, sensing hyper-spectral data, and classification of dynamic scenes from compressive data. Together, these applications demonstrate the effectiveness of the approach.

Connected through hyperedge HE-1 (induced by source paper "privacy preserving action recognition using coded aperture videos"):
  with members:
    - "towards privacy preserving visual recognition via adversarial training a pilot study"
    - "learning privacy preserving encodings through adversarial training"
    - "learning to anonymize faces for privacy preserving action detection"
    - "what have we learned from deep representations for action recognition"
    - "extreme low resolution activity recognition with multi siamese embedding learning"
    - "diffusercam lensless single exposure 3d imaging"
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "playing for data ground truth from computer games"

Connected through hyperedge HE-2 (induced by source paper "convex class model on symmetric positive definite manifolds"):
  with members:
    - "kernel sparse subspace clustering on symmetric positive definite manifolds"
    - "geometry aware similarity learning on spd manifolds for visual recognition"
    - "matching image sets via adaptive multi convex hull"
    - "kernel methods on the riemannian manifold of symmetric positive definite matrices"
    - "geodesic exponential kernels when curvature and linearity conflict"
    - "extrinsic methods for coding and dictionary learning on grassmann manifolds"
    - "sparse coding and dictionary learning for symmetric positive definite matrices a kernel approach"

Connected through hyperedge HE-3 (induced by source paper "localized lrr on grassmann manifolds an extrinsic view"):
  with members:
    - "low rank representation over the manifold of curves"
    - "expanding the family of grassmannian kernels an embedding perspective"
    - "from manifold to manifold geometry aware dimensionality reduction for spd matrices"
    - "dictionary learning and sparse coding on grassmann manifolds an extrinsic solution"
    - "robust recovery of subspace structures by low rank representation"
    - "sparse subspace clustering algorithm theory and applications"
    - "principal manifolds and nonlinear dimension reduction via local tangent space alignment"

Connected through hyperedge HE-4 (induced by source paper "temporal shape super resolution by intra frame motion encoding using high fps structured light"):
  with members:
    - "coded aperture compressive temporal imaging"
    - "depth extraction from video using non parametric sampling"

Connected through hyperedge HE-5 (induced by source paper "partial sum minimization of singular values representation on grassmann manifolds"):
  with members:
    - "convex sparse pca for unsupervised feature learning"
    - "laplacian lrr on product grassmann manifolds for human activity clustering in multi camera video surveillance"
    - "partial sum minimization of singular values in robust pca algorithm and applications"
    - "dictionary learning and sparse coding on grassmann manifolds an extrinsic solution"
    - "robust recovery of subspace structures by low rank representation"
    - "sparse subspace clustering algorithm theory and applications"
    - "the power of convex relaxation near optimal matrix completion"
    - "robust principal component analysis"

Connected through hyperedge HE-6 (induced by source paper "compressed learning a deep neural network approach"):
  with members:
    - "reconstruction free action inference from compressive imagers"

Connected through hyperedge HE-7 (induced by source paper "kernelized low rank representation on grassmann manifolds"):
  with members:
    - "expanding the family of grassmannian kernels an embedding perspective"
    - "optimizing over radial kernels on compact manifolds"
    - "dictionary learning and sparse coding on grassmann manifolds an extrinsic solution"
    - "robust recovery of subspace structures by low rank representation"
    - "sparse coding and dictionary learning for symmetric positive definite matrices a kernel approach"
    - "sparse subspace clustering algorithm theory and applications"
    - "robust principal component analysis"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-8 (induced by source paper "video compressive sensing for spatial multiplexing cameras using motion flow models"):
  with members:
    - "lisens a scalable architecture for video compressive sensing"
    - "fpa cs focal plane array based compressive imaging in short wave infrared"
    - "compressive coded aperture keyed exposure imaging with optical flow reconstruction"
    - "coded aperture compressive temporal imaging"
    - "the stone transform multi resolution image enhancement and real time compressive video"

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)