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: "log euclidean bag of words for human action recognition"
  Abstract: Representing videos by densely extracted local space-time features has recently become a popular approach for analysing actions. In this study, the authors tackle the problem of categorising human actions by devising bag of words (BoWs) models based on covariance matrices of spatiotemporal features, with the features formed from histograms of optical flow. Since covariance matrices form a special type of Riemannian manifold, the space of symmetric positive definite (SPD) matrices, non-Euclidean geometry should be taken into account while discriminating between covariance matrices. To this end, the authors propose to embed SPD manifolds to Euclidean spaces via a diffeomorphism and extend the BoW approach to its Riemannian version. The proposed BoW approach takes into account the manifold geometry of SPD matrices during the generation of the codebook and histograms. Experiments on challenging human action datasets show that the proposed method obtains notable improvements in discrimination accuracy, in comparison with several state-of-the-art methods.

Connected through hyperedge HE-1 (induced by source paper "multi camera action dataset for cross camera action recognition benchmarking"):
  with members:
    - "exploiting image trained cnn architectures for unconstrained video classification"
    - "towards open world recognition"
    - "a discriminative cnn video representation for event detection"
    - "ucf101 a dataset of 101 human actions classes from videos in the wild"
    - "frustratingly easy domain adaptation"

Connected through hyperedge HE-2 (induced by source paper "comparative evaluation of action recognition methods via riemannian manifolds fisher vectors and gmms ideal and challenging conditions"):
  with members:
    - "joint recognition and segmentation of actions via probabilistic integration of spatio temporal fisher vectors"
    - "bags of affine subspaces for robust object tracking"
    - "kernel methods on the riemannian manifold of symmetric positive definite matrices"
    - "learning discriminative stein kernel for spd matrices and its applications"
    - "multi action recognition via stochastic modelling of optical flow and gradients"
    - "random projections on manifolds of symmetric positive definite matrices for image classification"
    - "optimizing over radial kernels on compact manifolds"
    - "dictionary learning and sparse coding on grassmann manifolds an extrinsic solution"

Connected through hyperedge HE-3 (induced by source paper "multi camera action dataset mcad a dataset for studying non overlapped cross camera action recognition"):
  with members:
    - "exploiting image trained cnn architectures for unconstrained video classification"
    - "towards open world recognition"
    - "a discriminative cnn video representation for event detection"
    - "ucf101 a dataset of 101 human actions classes from videos in the wild"
    - "frustratingly easy domain adaptation"

Connected through hyperedge HE-4 (induced by source paper "efficient clustering on riemannian manifolds a kernelised random projection approach"):
  with members:
    - "kernel methods on the riemannian manifold of symmetric positive definite matrices"
    - "expanding the family of grassmannian kernels an embedding perspective"
    - "a framework for shape analysis via hilbert space embedding"
    - "random projections on manifolds of symmetric positive definite matrices for image classification"
    - "multi shot person re identification via relational stein divergence"
    - "ucf101 a dataset of 101 human actions classes from videos in the wild"
    - "random projections for k means clustering"

Connected through hyperedge HE-5 (induced by source paper "sparse coding on symmetric positive definite manifolds using bregman divergences"):
  with members:
    - "kernel methods on the riemannian manifold of symmetric positive definite matrices"
    - "from manifold to manifold geometry aware dimensionality reduction for spd matrices"
    - "dictionary learning and sparse coding on grassmann manifolds an extrinsic solution"
    - "spatio temporal covariance descriptors for action and gesture recognition"
    - "sparse coding and dictionary learning for symmetric positive definite matrices a kernel approach"

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)