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: "discriminative extended canonical correlation analysis for pattern set matching"
  Abstract: In this paper we address the problem of matching sets of vectors embedded in the same input space. We propose an approach which is motivated by canonical correlation analysis (CCA), a statistical technique which has proven successful in a wide variety of pattern recognition problems. Like CCA when applied to the matching of sets, our extended canonical correlation analysis (E-CCA) aims to extract the most similar modes of variability within two sets. Our first major contribution is the formulation of a principled framework for robust inference of such modes from data in the presence of uncertainty associated with noise and sampling randomness. E-CCA retains the efficiency and closed form computability of CCA, but unlike it, does not possess free parameters which cannot be inferred directly from data (inherent data dimensionality, and the number of canonical correlations used for set similarity computation). Our second major contribution is to show that in contrast to CCA, E-CCA is readily adapted to match sets in a discriminative learning scheme which we call discriminative extended canonical correlation analysis (DE-CCA). Theoretical contributions of this paper are followed by an empirical evaluation of its premises on the task of face recognition from sets of rasterized appearance images. The results demonstrate that our approach, E-CCA, already outperforms both CCA and its quasi-discriminative counterpart constrained CCA (C-CCA), for all values of their free parameters. An even greater improvement is achieved with the discriminative variant, DE-CCA.

Connected through hyperedge HE-1 (induced by source paper "achieving stable subspace clustering by post processing generic clustering results"):
  with members:
    - "a framework for improving the performance of verification algorithms with a low false positive rate requirement and limited training data"
    - "hallucinating optimal high dimensional subspaces"
    - "sparse subspace clustering algorithm theory and applications"
    - "a geometric analysis of subspace clustering with outliers"
    - "contextually learnt detection of unusual motion based behaviour in crowded public spaces"
    - "robust pca via outlier pursuit"
    - "robust principal component analysis"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-2 (induced by source paper "descriptor transition tables for object retrieval using unconstrained cluttered video acquired using a consumer level handheld mobile device"):
  with members:
    - "discovering topic structures of a temporally evolving document corpus"
    - "a framework for improving the performance of verification algorithms with a low false positive rate requirement and limited training data"
    - "matching objects across the textured smooth continuum"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"
    - "unsupervised discovery of mid level discriminative patches"

Connected through hyperedge HE-3 (induced by source paper "learnt quasi transitive similarity for retrieval from large collections of faces"):
  with members:
    - "a framework for improving the performance of verification algorithms with a low false positive rate requirement and limited training data"
    - "hallucinating optimal high dimensional subspaces"

Connected through hyperedge HE-4 (induced by source paper "automatic vehicle tracking and recognition from aerial image sequences"):
  with members:
    - "groupwise registration of aerial images"
    - "matching objects across the textured smooth continuum"
    - "contextually learnt detection of unusual motion based behaviour in crowded public spaces"
    - "multiple object tracking in cluttered and crowded public spaces"

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)