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: "geometry aware similarity learning on spd manifolds for visual recognition"
  Abstract: Symmetric Positive Definite (SPD) matrices have been widely used for data representation in many visual recognition tasks. The success mainly attributes to learning discriminative SPD matrices with encoding the Riemannian geometry of the underlying SPD manifold. In this paper, we propose a geometry-aware SPD similarity learning (SPDSL) framework to learn discriminative SPD features by directly pursuing manifold-manifold transformation matrix of column full-rank. Specifically, by exploiting the Riemannian geometry of the manifold of fixed-rank Positive Semidefinite (PSD) matrices, we present a new solution to reduce optimizing over the space of column full-rank transformation matrices to optimizing on the PSD manifold which has a well-established Riemannian structure. Under this solution, we exploit a new supervised SPD similarity learning technique to learn the transformation by regressing the similarities of selected SPD data pairs to their ground-truth similarities on the target SPD manifold. To optimize the proposed objective function, we further derive an algorithm on the PSD manifold. Evaluations on three visual classification tasks show the advantages of the proposed approach over the existing SPD-based discriminant learning methods.

Connected through hyperedge HE-1 (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-2 (induced by source paper "riemannian joint dimensionality reduction and dictionary learning on symmetric positive definite manifold"):
  with members:
    - "dimensionality reduction on spd manifolds the emergence of geometry aware methods"
    - "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-3 (induced by source paper "convex class model on symmetric positive definite manifolds"):
  with members:
    - "kernel sparse subspace clustering on symmetric positive definite manifolds"
    - "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"
    - "compressive acquisition of dynamic scenes"

Connected through hyperedge HE-4 (induced by source paper "learning a robust representation via a deep network on symmetric positive definite manifolds"):
  with members:
    - "deep manifold to manifold transforming network for action recognition"
    - "deepkspd learning kernel matrix based spd representation for fine grained image recognition"
    - "is second order information helpful for large scale visual recognition"
    - "second order convolutional neural networks"
    - "dimensionality reduction on spd manifolds the emergence of geometry aware methods"
    - "exploiting structure sparsity for covariance based visual representation"
    - "deep ten texture encoding network"
    - "learning an invariant hilbert space for domain adaptation"

Connected through hyperedge HE-5 (induced by source paper "building deep networks on grassmann manifolds"):
  with members:
    - "generalized rank pooling for activity recognition"
    - "elastic functional coding of riemannian trajectories"
    - "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"

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)