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 "a survey on metric learning for feature vectors and structured data"):
  Abstract: The need for appropriate ways to measure the distance or similarity between data is ubiquitous in machine learning, pattern recognition and data mining, but handcrafting such good metrics for specific problems is generally difficult. This has led to the emergence of metric learning, which aims at automatically learning a metric from data and has attracted a lot of interest in machine learning and related fields for the past ten years. This survey paper proposes a systematic review of the metric learning literature, highlighting the pros and cons of each approach. We pay particular attention to Mahalanobis distance metric learning, a well-studied and successful framework, but additionally present a wide range of methods that have recently emerged as powerful alternatives, including nonlinear metric learning, similarity learning and local metric learning. Recent trends and extensions, such as semi-supervised metric learning, metric learning for histogram data and the derivation of generalization guarantees, are also covered. Finally, this survey addresses metric learning for structured data, in particular edit distance learning, and attempts to give an overview of the remaining challenges in metric learning for the years to come.

Includes members:
  Member "robustness and generalization for metric learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "revisiting training strategies and generalization performance in deep metric learning")
  Member "efficient distance metric learning by adaptive sampling and mini batch stochastic gradient descent sgd":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "fast low rank metric learning for large scale and high dimensional data")
  Member "large margin metric learning for partitioning problems":
    also connected through other hyperedges:
      (none)
  Member "on the generalization ability of online learning algorithms for pairwise loss functions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "stability and optimization error of stochastic gradient descent for pairwise learning")
  Member "guaranteed classification via regularized similarity learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (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-5 (induced by source paper "contrastive multiview coding")
  Member "parametric local metric learning for nearest neighbor classification":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "local distance metric learning for nearest neighbor algorithm")
  Member "a conditional random field for discriminatively trained finite state string edit distance":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "metric learning for ordered labeled trees with pq grams")

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)