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: "bregman divergences for infinite dimensional covariance matrices"
  Abstract: We introduce an approach to computing and comparing Covariance Descriptors (CovDs) in infinite-dimensional spaces. CovDs have become increasingly popular to address classification problems in computer vision. While CovDs offer some robustness to measurement variations, they also throw away part of the information contained in the original data by only retaining the second-order statistics over the measurements. Here, we propose to overcome this limitation by first mapping the original data to a high-dimensional Hilbert space, and only then compute the CovDs. We show that several Bregman divergences can be computed between the resulting CovDs in Hilbert space via the use of kernels. We then exploit these divergences for classification purposes. Our experiments demonstrate the benefits of our approach on several tasks, such as material and texture recognition, person re-identification, and action recognition from motion capture data.

Connected through hyperedge HE-1 (induced by source paper "more about covariance descriptors for image set coding log euclidean framework based kernel matrix representation"):
  with members:
    - "component spd matrices a low dimensional discriminative data descriptor for image set classification"
    - "multiple manifolds metric learning with application to image set classification"
    - "a riemannian network for spd matrix learning"

Connected through hyperedge HE-2 (induced by source paper "scalable and compact 3d action recognition with approximated rbf kernel machines"):
  with members:
    - "a compact kernel approximation for 3d action recognition"
    - "a new representation of skeleton sequences for 3d action recognition"
    - "joint distance maps based action recognition with convolutional neural networks"
    - "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"
    - "action recognition based on joint trajectory maps using convolutional neural networks"
    - "deep learning on lie groups for skeleton based action recognition"

Connected through hyperedge HE-3 (induced by source paper "when kernel methods meet feature learning log covariance network for action recognition from skeletal data"):
  with members:
    - "joint distance maps based action recognition with convolutional neural networks"
    - "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"
    - "action recognition based on joint trajectory maps using convolutional neural networks"
    - "deep learning on lie groups for skeleton based action recognition"
    - "tensor representations via kernel linearization for action recognition from 3d skeletons"
    - "a riemannian network for spd matrix learning"

Connected through hyperedge HE-4 (induced by source paper "deepkspd learning kernel matrix based spd representation for fine grained image recognition"):
  with members:
    - "is second order information helpful for large scale visual recognition"
    - "improved bilinear pooling with cnns"
    - "low rank bilinear pooling for fine grained classification"
    - "bilinear cnns for fine grained visual recognition"
    - "compact bilinear pooling"
    - "bilinear cnn models for fine grained visual recognition"
    - "kernel methods on the riemannian manifold of symmetric positive definite matrices"
    - "convolutional kernel networks"

Connected through hyperedge HE-5 (induced by source paper "a compact kernel approximation for 3d action recognition"):
  with members:
    - "kernelized covariance for action recognition"
    - "tensor representations via kernel linearization for action recognition from 3d skeletons"
    - "classifying and visualizing motion capture sequences using deep neural networks"
    - "random feature maps for dot product kernels"

Connected through hyperedge HE-6 (induced by source paper "geometry aware similarity learning on spd manifolds for visual recognition"):
  with members:
    - "dimensionality reduction on spd manifolds the emergence of geometry aware methods"
    - "a riemannian network for spd matrix learning"
    - "supervised logeuclidean metric learning for symmetric positive definite matrices"
    - "kernel methods on the riemannian manifold of symmetric positive definite matrices"
    - "from manifold to manifold geometry aware dimensionality reduction for spd matrices"
    - "sparse coding on symmetric positive definite manifolds using bregman divergences"
    - "sparse coding and dictionary learning for symmetric positive definite matrices a kernel approach"
    - "algorithms for learning kernels based on centered alignment"

Connected through hyperedge HE-7 (induced by source paper "tensor representations via kernel linearization for action recognition from 3d skeletons"):
  with members:
    - "kernelized covariance for action recognition"
    - "tensor representations via kernel linearization for action recognition from 3d skeletons extended version"
    - "convolutional kernel networks"

Connected through hyperedge HE-8 (induced by source paper "kernelized covariance for action recognition"):
  with members:
    - "kernel methods on the riemannian manifold of symmetric positive definite matrices"

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)