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: "k tangent spaces on riemannian manifolds for improved pedestrian detection"
  Abstract: For covariance-based image descriptors, taking into account the curvature of the corresponding feature space has been shown to improve discrimination performance. This is often done through representing the descriptors as points on Riemannian manifolds, with the discrimination accomplished on a tangent space. However, such treatment is restrictive as distances between arbitrary points on the tangent space do not represent true geodesic distances, and hence do not represent the manifold structure accurately. In this paper we propose a general discriminative model based on the combination of several tangent spaces, in order to preserve more details of the structure. The model can be used as a weak learner in a boosting-based pedestrian detection framework. Experiments on the challenging INRIA and DaimlerChrysler datasets show that the proposed model leads to considerably higher performance than methods based on histograms of oriented gradients as well as previous Riemannian-based techniques.

Connected through hyperedge HE-1 (induced by source paper "random projections on manifolds of symmetric positive definite matrices for image classification"):
  with members:
    - "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"
    - "is margin preserved after random projection"

Connected through hyperedge HE-2 (induced by source paper "multi shot person re identification via relational stein divergence"):
  with members:
    - "improved foreground detection via block based classifier cascade with probabilistic decision integration"
    - "spatio temporal covariance descriptors for action and gesture recognition"
    - "sparse coding and dictionary learning for symmetric positive definite matrices a kernel approach"

Connected through hyperedge HE-3 (induced by source paper "spatio temporal covariance descriptors for action and gesture recognition"):
  with members:
    - "sparse coding and dictionary learning for symmetric positive definite matrices a kernel approach"
    - "improved anomaly detection in crowded scenes via cell based analysis of foreground speed size and texture"

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)