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: "elastic functional coding of riemannian trajectories"
  Abstract: Visual observations of dynamic phenomena, such as human actions, are often represented as sequences of smoothly-varying features. In cases where the feature spaces can be structured as Riemannian manifolds, the corresponding representations become trajectories on manifolds. Analysis of these trajectories is challenging due to non-linearity of underlying spaces and high-dimensionality of trajectories. In vision problems, given the nature of physical systems involved, these phenomena are better characterized on a low-dimensional manifold compared to the space of Riemannian trajectories. For instance, if one does not impose physical constraints of the human body, in data involving human action analysis, the resulting representation space will have highly redundant features. Learning an effective, low-dimensional embedding for action representations will have a huge impact in the areas of search and retrieval, visualization, learning, and recognition. Traditional manifold learning addresses this problem for static points in the euclidean space, but its extension to Riemannian trajectories is non-trivial and remains unexplored. The difficulty lies in inherent non-linearity of the domain and temporal variability of actions that can distort any traditional metric between trajectories. To overcome these issues, we use the framework based on transported square-root velocity fields (TSRVF); this framework has several desirable properties, including a rate-invariant metric and vector space representations. We propose to learn an embedding such that each action trajectory is mapped to a single point in a low-dimensional euclidean space, and the trajectories that differ only in temporal rates map to the same point. We utilize the TSRVF representation, and accompanying statistical summaries of Riemannian trajectories, to extend existing coding methods such as PCA, KSVD and Label Consistent KSVD to Riemannian trajectories or more generally to Riemannian functions. We show that such coding efficiently captures trajectories in applications such as action recognition, stroke rehabilitation, visual speech recognition, clustering and diverse sequence sampling. Using this framework, we obtain state-of-the-art recognition results, while reducing the dimensionality/complexity by a factor of   $100-250\times$      . Since these mappings and codes are invertible, they can also be used to interactively visualize Riemannian trajectories and synthesize actions.

Connected through hyperedge HE-1 (induced by source paper "fitting comparison and alignment of trajectories on positive semi definite matrices with application to action recognition"):
  with members:
    - "uav gesture a dataset for uav control and gesture recognition"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "human action recognition and prediction a survey"
    - "deep covariance descriptors for facial expression recognition"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "optimized skeleton based action recognition via sparsified graph regression"
    - "a novel geometric framework on gram matrix trajectories for human behavior understanding"
    - "automatic analysis of facial expressions based on deep covariance trajectories"

Connected through hyperedge HE-2 (induced by source paper "a novel geometric framework on gram matrix trajectories for human behavior understanding"):
  with members:
    - "emotion recognition by body movement representation on the manifold of symmetric positive definite matrices"
    - "kernelized covariance for action recognition"
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "spatio temporal lstm with trust gates for 3d human action recognition"
    - "co occurrence feature learning for skeleton based action recognition using regularized deep lstm networks"
    - "an end to end spatio temporal attention model for human action recognition from skeleton data"
    - "shape distributions of nonlinear dynamical systems for video based inference"
    - "differential recurrent neural networks for action recognition"

Connected through hyperedge HE-3 (induced by source paper "robust 3d action recognition through sampling local appearances and global distributions"):
  with members:
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "differential recurrent neural networks for action recognition"
    - "histogram of oriented principal components for cross view action recognition"
    - "a discussion on the validation tests employed to compare human action recognition methods using the msr action3d dataset"
    - "mining mid level features for action recognition based on effective skeleton representation"
    - "learning human activities and object affordances from rgb d videos"
    - "unstructured human activity detection from rgbd images"

Connected through hyperedge HE-4 (induced by source paper "a novel space time representation on the positive semidefinite con for facial expression recognition"):
  with members:
    - "facenet2expnet regularizing a deep face recognition net for expression recognition"
    - "survey on rgb 3d thermal and multimodal approaches for facial expression recognition history trends and affect related applications"
    - "kernel methods on riemannian manifolds with gaussian rbf kernels"
    - "spatio temporal covariance descriptors for action and gesture recognition"

Connected through hyperedge HE-5 (induced by source paper "building deep networks on grassmann manifolds"):
  with members:
    - "generalized rank pooling for activity recognition"
    - "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"
    - "kernel methods on riemannian manifolds with gaussian rbf kernels"

Connected through hyperedge HE-6 (induced by source paper "deep learning on lie groups for skeleton based action recognition"):
  with members:
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "building deep networks on grassmann manifolds"
    - "spatio temporal lstm with trust gates for 3d human action recognition"
    - "space time representation of people based on 3d skeletal data a review"
    - "a riemannian network for spd matrix learning"
    - "geodesic convolutional neural networks on riemannian manifolds"
    - "structural rnn deep learning on spatio temporal graphs"
    - "spectral networks and locally connected networks on 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)