human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "guaranteed classification via regularized similarity learning"):
  Abstract: Learning an appropriate (dis)similarity function from the available data is a central problem in machine learning, since the success of many machine learning algorithms critically depends on the choice of a similarity function to compare examples. Despite many approaches for similarity metric learning have been proposed, there is little theoretical study on the links between similarity met- ric learning and the classification performance of the result classifier. In this paper, we propose a regularized similarity learning formulation associated with general matrix-norms, and establish their generalization bounds. We show that the generalization error of the resulting linear separator can be bounded by the derived generalization bound of similarity learning. This shows that a good gen- eralization of the learnt similarity function guarantees a good classification of the resulting linear classifier. Our results extend and improve those obtained by Bellet at al. [3]. Due to the techniques dependent on the notion of uniform stability [6], the bound obtained there holds true only for the Frobenius matrix- norm regularization. Our techniques using the Rademacher complexity [5] and its related Khinchin-type inequality enable us to establish bounds for regularized similarity learning formulations associated with general matrix-norms including sparse L 1 -norm and mixed (2,1)-norm.

Includes members:
  Member "generalization guarantees for a binary classification framework for two stage multiple kernel learning":
    also connected through other hyperedges:
      (none)
  Member "similarity learning for provably accurate sparse linear classification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "contrastive multiview coding")
  Member "supervised learning with similarity functions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "metric and non metric proximity transformations at linear costs")
  Member "generalization bounds for metric and similarity learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "supervised categorical metric learning with schatten p norms")
  Member "a binary classification framework for two stage multiple kernel learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on the generalization ability of online learning algorithms for pairwise loss functions")
  Member "similarity based learning via data driven embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "constraint selection in metric learning")
  Member "regularization techniques for learning with matrices":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "distributed learning with sublinear communication")

Question: Please tell me which class the source paper 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)