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: "deepkspd learning kernel matrix based spd representation for fine grained image recognition"
  Abstract: Being symmetric positive-definite (SPD), covariance matrix has traditionally been used to represent a set of local descriptors in visual recognition. Recent study shows that kernel matrix can give considerably better representation by modelling the nonlinearity in the local descriptor set. Nevertheless, neither the descriptors nor the kernel matrix is deeply learned. Worse, they are considered separately, hindering the pursuit of an optimal SPD representation. This work proposes a deep network that jointly learns local descriptors, kernel-matrix-based SPD representation, and the classifier via an end-to-end training process. We derive the derivatives for the mapping from a local descriptor set to the SPD representation to carry out backpropagation. Also, we exploit the Daleckii-Krein formula in operator theory to give a concise and unified result on differentiating SPD matrix functions, including the matrix logarithm to handle the Riemannian geometry of kernel matrix. Experiments not only show the superiority of kernel-matrix-based SPD representation with deep local descriptors, but also verify the advantage of the proposed deep network in pursuing better SPD representations for fine-grained image recognition tasks.

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"
    - "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"
    - "dimensionality reduction on spd manifolds the emergence of geometry aware methods"

Connected through hyperedge HE-2 (induced by source paper "skeleton based hand gesture recognition by learning spd matrices with neural networks"):
  with members:
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "graph edge convolutional neural networks for skeleton based action recognition"
    - "spatio temporal graph convolution for skeleton based action recognition"
    - "feastnet feature steered graph convolutions for 3d shape analysis"
    - "is second order information helpful for large scale visual recognition"
    - "motion feature augmented recurrent neural network for skeleton based dynamic hand gesture recognition"
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "building deep networks on grassmann manifolds"

Connected through hyperedge HE-3 (induced by source paper "riemannian batch normalization for spd neural networks"):
  with members:
    - "a statistical recurrent model on the manifold of symmetric positive definite matrices"
    - "manifoldnet a deep network framework for manifold valued data"
    - "learning a robust representation via a deep network on symmetric positive definite manifolds"
    - "when kernel methods meet feature learning log covariance network for action recognition from skeletal data"
    - "towards faster training of global covariance pooling networks by iterative matrix square root normalization"
    - "building deep networks on grassmann manifolds"
    - "deep learning on lie groups for skeleton based action recognition"
    - "a riemannian network for spd matrix learning"

Connected through hyperedge HE-4 (induced by source paper "multi task kernel null space for one class classification"):
  with members:
    - "one class kernel spectral regression"
    - "one class kernel spectral regression for outlier detection"
    - "fast multi output relevance vector regression"
    - "a survey on multi task learning"
    - "convex learning of multiple tasks and their structure"

Connected through hyperedge HE-5 (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"
    - "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"
    - "geometry aware similarity learning on spd manifolds for visual recognition"

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)