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: "combined learning of salient local descriptors and distance metrics for image set face verification"
  Abstract: In contrast to comparing faces via single exemplars, matching sets of face images increases robustness and discrimination performance. Recent image set matching approaches typically measure similarities between subspaces or manifolds, while representing faces in a rigid and holistic manner. Such representations are easily affected by variations in terms of alignment, illumination, pose and expression. While local feature based representations are considerably more robust to such variations, they have received little attention within the image set matching area. We propose a novel image set matching technique, comprised of three aspects: (i) robust descriptors of face regions based on local features, partly inspired by the hierarchy in the human visual system, (ii) use of several subspace and exemplar metrics to compare corresponding face regions, (iii) jointly learning which regions are the most discriminative while finding the optimal mixing weights for combining metrics. Experiments on LFW, PIE and MOBIO face datasets show that the proposed algorithm obtains considerably better performance than several recent state of-the-art techniques, such as Local Principal Angle and the Kernel Affine Hull Method.

Connected through hyperedge HE-1 (induced by source paper "bags of affine subspaces for robust object tracking"):
  with members:
    - "extrinsic methods for coding and dictionary learning on grassmann manifolds"
    - "object tracking via non euclidean geometry a grassmann approach"
    - "dictionary learning and sparse coding on grassmann manifolds an extrinsic solution"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-2 (induced by source paper "extrinsic methods for coding and dictionary learning on grassmann manifolds"):
  with members:
    - "bags of affine subspaces for robust object tracking"
    - "sparse coding on symmetric positive definite manifolds using bregman divergences"
    - "dictionary learning and sparse coding on grassmann manifolds an extrinsic solution"
    - "sparse subspace clustering algorithm theory and applications"
    - "compressive acquisition of dynamic scenes"

Connected through hyperedge HE-3 (induced by source paper "object tracking via non euclidean geometry a grassmann approach"):
  with members:
    - "dictionary learning and sparse coding on grassmann manifolds an extrinsic solution"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-4 (induced by source paper "dictionary learning and sparse coding on grassmann manifolds an extrinsic solution"):
  with members:
    - "sparse coding and dictionary learning for symmetric positive definite matrices a kernel approach"

Connected through hyperedge HE-5 (induced by source paper "k tangent spaces on riemannian manifolds for improved pedestrian detection"):
  with members:
    - "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)