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 "robustness and generalization for metric learning"):
  Abstract: Abstract   Metric learning has attracted a lot of interest over the last decade, but the generalization ability of such methods has not been thoroughly studied. In this paper, we introduce an adaptation of the notion of algorithmic robustness (previously introduced by Xu and Mannor) that can be used to derive generalization bounds for metric learning. We further show that a weak notion of robustness is in fact a necessary and sufficient condition for a metric learning algorithm to generalize. To illustrate the applicability of the proposed framework, we derive generalization results for a large family of existing metric learning algorithms, including some sparse formulations that are not covered by the previous results.

Includes members:
  Member "sparse compositional metric learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multiple metric learning for structured data")
  Member "a survey on metric learning for feature vectors and structured data":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "an overview of distance and similarity functions for structured data")
  Member "guaranteed classification via regularized similarity learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "temporal model adaptation for person re identification")
  Member "similarity learning for provably accurate sparse linear classification":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "contrastive multiview coding")
  Member "generalization bounds for metric and similarity learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "supervised categorical metric learning with schatten p norms")
  Member "similarity based learning via data driven embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "constraint selection in metric learning")
  Member "robustness and generalization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "understanding generalization in deep learning via tensor methods")

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)