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: "dimensionality reduction on spd manifolds the emergence of geometry aware methods"
  Abstract: Representing images and videos with Symmetric Positive Definite (SPD) matrices, and considering the Riemannian geometry of the resulting space, has been shown to yield high discriminative power in many visual recognition tasks. Unfortunately, computation on the Riemannian manifold of SPD matrices -especially of high-dimensional ones- comes at a high cost that limits the applicability of existing techniques. In this paper, we introduce algorithms able to handle high-dimensional SPD matrices by constructing a lower-dimensional SPD manifold. To this end, we propose to model the mapping from the high-dimensional SPD manifold to the low-dimensional one with an orthonormal projection. This lets us formulate dimensionality reduction as the problem of finding a projection that yields a low-dimensional manifold either with maximum discriminative power in the supervised scenario, or with maximum variance of the data in the unsupervised one. We show that learning can be expressed as an optimization problem on a Grassmann manifold and discuss fast solutions for special cases. Our evaluation on several classification tasks evidences that our approach leads to a significant accuracy gain over state-of-the-art methods.

Connected through hyperedge HE-1 (induced by source paper "hallucinating idt descriptors and i3d optical flow features for action recognition with cnns"):
  with members:
    - "loss switching fusion with similarity search for video classification"
    - "a comparative review of recent kinect based action recognition algorithms"
    - "long term feature banks for detailed video understanding"
    - "learning discriminative video representations using adversarial perturbations"
    - "cnn based action recognition and supervised domain adaptation on 3d body skeletons via kernel feature maps"
    - "a deeper look at power normalizations"
    - "non linear temporal subspace representations for activity recognition"
    - "higher order pooling of cnn features via kernel linearization for action recognition"

Connected through hyperedge HE-2 (induced by source paper "a neural network based on spd manifold learning for skeleton based hand gesture recognition"):
  with members:
    - "spatio temporal graph convolution for skeleton based action recognition"
    - "modeling temporal dynamics and spatial configurations of actions using two stream recurrent neural networks"
    - "deepkspd learning kernel matrix based spd representation for fine grained image recognition"
    - "is second order information helpful for large scale visual recognition"
    - "motion feature augmented recurrent neural network for skeleton based dynamic hand gesture recognition"
    - "a new representation of skeleton sequences for 3d action recognition"
    - "first person hand action benchmark with rgb d videos and 3d hand pose annotations"
    - "ntu rgb d a large scale dataset for 3d human activity analysis"

Connected through hyperedge HE-3 (induced by source paper "adaptive stochastic gradient algorithms on riemannian manifolds"):
  with members:
    - "riemannian stochastic variance reduced gradient algorithm with retraction and vector transport"
    - "on the convergence of adam and beyond"
    - "on the convergence of adaptive gradient methods for nonconvex optimization"
    - "riemannian adaptive optimization methods"
    - "learning continuous hierarchies in the lorentz model of hyperbolic geometry"
    - "averaging stochastic gradient descent on riemannian manifolds"
    - "on the convergence of a class of adam type algorithms for non convex optimization"
    - "hyperbolic entailment cones for learning hierarchical embeddings"

Connected through hyperedge HE-4 (induced by source paper "riemannian joint dimensionality reduction and dictionary learning on symmetric positive definite manifold"):
  with members:
    - "geometry aware similarity learning on spd manifolds for visual recognition"
    - "a riemannian network for spd matrix learning"
    - "jointly learning non negative projection and dictionary with discriminative graph constraints for classification"
    - "manopt a matlab toolbox for optimization on manifolds"

Connected through hyperedge HE-5 (induced by source paper "analyzing dynamical brain functional connectivity as trajectories on space of covariance matrices"):
  with members:
    - "locality preserving projection on spd matrix lie group algorithm and analysis"
    - "manopt a matlab toolbox for optimization on manifolds"

Connected through hyperedge HE-6 (induced by source paper "multiple riemannian manifold valued descriptors based image set classification with multi kernel metric learning"):
  with members:
    - "deep learning on lie groups for skeleton based action recognition"

Connected through hyperedge HE-7 (induced by source paper "metrics induced by jensen shannon and related divergences on positive definite matrices"):
  with members:
    - "the burbea rao and bhattacharyya centroids"

Connected through hyperedge HE-8 (induced by source paper "learning representations for multivariate time series with missing data using temporal kernelized autoencoders"):
  with members:
    - "making the dynamic time warping distance warping invariant"
    - "a deep neural network for unsupervised anomaly detection and diagnosis in multivariate time series data"
    - "a dual stage attention based recurrent neural network for time series prediction"
    - "structured attention networks"
    - "an overview and comparative analysis of recurrent neural networks for short term load forecasting"
    - "mida multiple imputation using denoising autoencoders"
    - "multiplex visibility graphs to investigate recurrent neural network dynamics"
    - "optimal hyperparameters for deep lstm networks for sequence labeling tasks"

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)