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: "expanding the family of grassmannian kernels an embedding perspective"
  Abstract: Modeling videos and image-sets as linear subspaces has proven beneficial for many visual recognition tasks. However, it also incurs challenges arising from the fact that linear subspaces do not obey Euclidean geometry, but lie on a special type of Riemannian manifolds known as Grassmannian. To leverage the techniques developed for Euclidean spaces (e.g, support vector machines) with subspaces, several recent studies have proposed to embed the Grassmannian into a Hilbert space by making use of a positive definite kernel. Unfortunately, only two Grassmannian kernels are known, none of which -as we will show- is universal, which limits their ability to approximate a target function arbitrarily well. Here, we introduce several positive definite Grassmannian kernels, including universal ones, and demonstrate their superiority over previously-known kernels in various tasks, such as classification, clustering, sparse coding and hashing.

Connected through hyperedge HE-1 (induced by source paper "learning discriminative video representations using adversarial perturbations"):
  with members:
    - "non linear temporal subspace representations for activity recognition"
    - "video representation learning using discriminative pooling"
    - "deep unsupervised saliency detection a multiple noisy labeling perspective"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "generalized rank pooling for activity recognition"
    - "interpretable 3d human action analysis with temporal convolutional networks"
    - "safetynet detecting and rejecting adversarial examples robustly"
    - "lattice long short term memory for human action recognition"

Connected through hyperedge HE-2 (induced by source paper "non linear temporal subspace representations for activity recognition"):
  with members:
    - "video representation learning using discriminative pooling"
    - "higher order pooling of cnn features via kernel linearization for action recognition"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "the kinetics human action video dataset"
    - "rank pooling for action recognition"
    - "ordered pooling of optical flow sequences for action recognition"
    - "kernelized covariance for action recognition"
    - "convolutional pose machines"

Connected through hyperedge HE-3 (induced by source paper "grassmannian learning embedding geometry awareness in shallow and deep learning"):
  with members:
    - "geometric deep learning going beyond euclidean data"
    - "learning invariant riemannian geometric representations using deep nets"
    - "pymanopt a python toolbox for optimization on manifolds using automatic differentiation"
    - "geometric deep learning on graphs and manifolds using mixture model cnns"
    - "learning an invariant hilbert space for domain adaptation"
    - "geodesic convolutional neural networks on riemannian manifolds"
    - "very deep convolutional networks for large scale image recognition"
    - "intriguing properties of neural networks"

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"
    - "persistent homology of attractors for action recognition"
    - "geodesic convolutional neural networks on riemannian manifolds"

Connected through hyperedge HE-5 (induced by source paper "generalized rank pooling for activity recognition"):
  with members:
    - "higher order pooling of cnn features via kernel linearization for action recognition"
    - "rank pooling for action recognition"
    - "ordered pooling of optical flow sequences for action recognition"
    - "temporal segment networks towards good practices for deep action recognition"
    - "convolutional two stream network fusion for video action recognition"
    - "spatiotemporal residual networks for video action recognition"
    - "on differentiating parameterized argmin and argmax problems with application to bi level optimization"
    - "the multiverse loss for robust transfer learning"

Connected through hyperedge HE-6 (induced by source paper "localized lrr on grassmann manifolds an extrinsic view"):
  with members:
    - "low rank representation over the manifold of curves"
    - "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"
    - "compressive acquisition of dynamic scenes"
    - "principal manifolds and nonlinear dimension reduction via local tangent space alignment"

Connected through hyperedge HE-7 (induced by source paper "dimensionality reduction on grassmannian via riemannian optimization a generalized perspective"):
  with members:
    - "locality preserving projections for grassmann manifold"
    - "matconvnet convolutional neural networks for matlab"
    - "dictionary learning and sparse coding on grassmann manifolds an extrinsic solution"
    - "manopt a matlab toolbox for optimization on manifolds"
    - "ucf101 a dataset of 101 human actions classes from videos in the wild"

Connected through hyperedge HE-8 (induced by source paper "building deep networks on grassmann manifolds"):
  with members:
    - "generalized rank pooling for activity recognition"
    - "elastic functional coding of riemannian trajectories"
    - "geometry aware similarity learning on spd manifolds for visual recognition"
    - "a riemannian network for spd matrix learning"
    - "deep convolutional networks on graph structured data"
    - "geodesic convolutional neural networks on riemannian manifolds"
    - "training deep networks with structured layers by matrix backpropagation"
    - "structural rnn deep learning on spatio temporal graphs"

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)