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: "a framework for improving the performance of verification algorithms with a low false positive rate requirement and limited training data"
  Abstract: In this paper we address the problem of matching patterns in the so-called verification setting in which a novel, query pattern is verified against a single training pattern: the decision sought is whether the two match (i.e. belong to the same class) or not. Unlike previous work which has universally focused on the development of more discriminative distance functions between patterns, here we consider the equally important and pervasive task of selecting a distance threshold which fits a particular operational requirement - specifically, the target false positive rate (FPR). First, we argue on theoretical grounds that a data-driven approach is inherently ill-conditioned when the desired FPR is low, because by the very nature of the challenge only a small portion of training data affects or is affected by the desired threshold. This leads us to propose a general, statistical model-based method instead. Our approach is based on the interpretation of an inter-pattern distance as implicitly defining a pattern embedding which approximately distributes patterns according to an isotropic multi-variate normal distribution in some space. This interpretation is then used to show that the distribution of training inter-pattern distances is the non-central chi2 distribution, differently parameterized for each class. Thus, to make the class-specific threshold choice we propose a novel analysis-by-synthesis iterative algorithm which estimates the three free parameters of the model (for each class) using task-specific constraints. The validity of the premises of our work and the effectiveness of the proposed method are demonstrated by applying the method to the task of set-based face verification on a large database of pseudo-random head motion videos.

Connected through hyperedge HE-1 (induced by source paper "achieving stable subspace clustering by post processing generic clustering results"):
  with members:
    - "hallucinating optimal high dimensional subspaces"
    - "discriminative extended canonical correlation analysis for pattern set matching"
    - "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"
    - "discriminative extended canonical correlation analysis for pattern set matching"
    - "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:
    - "hallucinating optimal high dimensional subspaces"
    - "discriminative extended canonical correlation analysis for pattern set matching"

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)