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: "learning discriminative metrics via generative models and kernel learning"
  Abstract: AbstractMetrics specifying distances between data points can be learned in a discriminative manner or fromgenerative models. In this paper, we show how to unify generative and discriminative learning of met-rics via a kernel learning framework. Speciﬁcally, we learn local metrics optimized from parametricgenerative models. These are then used as base kernels to construct a global kernel that minimizes adiscriminative training criterion. We consider both linear and nonlinear combinations of local metrickernels. Our empirical results show that these combinations signiﬁcantly improve performance on clas-siﬁcation tasks. The proposed learning algorithm is also ve ry efﬁcient, achieving order of magnitudespeedup in training time compared to previous discriminative baseline methods. 1 Introduction Metric learning – learning how to specify distances between data points – has been a topic of much interestin machine learning recently. For example, discriminative techniques for metric learning aim to improvethe performance of a classiﬁer, such as the k-nearest neighbor classiﬁer, on a training set. As a generalstrategy, these techniques try to reduce the distances between data points belonging to the same class whileincreasing the distances between data points from different classes [1, 2, 3, 4, 5, 6, 7, 8]. In this framework,1

Connected through hyperedge HE-1 (induced by source paper "a survey on metric learning for feature vectors and structured data"):
  with members:
    - "robustness and generalization for metric learning"
    - "efficient distance metric learning by adaptive sampling and mini batch stochastic gradient descent sgd"
    - "large margin metric learning for partitioning problems"
    - "on the generalization ability of online learning algorithms for pairwise loss functions"
    - "guaranteed classification via regularized similarity learning"
    - "similarity learning for provably accurate sparse linear classification"
    - "parametric local metric learning for nearest neighbor classification"
    - "a conditional random field for discriminatively trained finite state string edit distance"

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.LG(Machine Learning)