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 kernel classification framework for metric learning"):
  Abstract: Learning a distance metric from the given training samples plays a crucial role in many machine learning tasks, and various models and optimization algorithms have been proposed in the past decade. In this paper, we generalize several state-of-the-art metric learning methods, such as large margin nearest neighbor (LMNN) and information theoretic metric learning (ITML), into a kernel classification framework. First, doublets and triplets are constructed from the training samples, and a family of degree-2 polynomial kernel functions are proposed for pairs of doublets or triplets. Then, a kernel classification framework is established, which can not only generalize many popular metric learning methods such as LMNN and ITML, but also suggest new metric learning methods, which can be efficiently implemented, interestingly, by using the standard support vector machine (SVM) solvers. Two novel metric learning methods, namely doublet-SVM and triplet-SVM, are then developed under the proposed framework. Experimental results show that doublet-SVM and triplet-SVM achieve competitive classification accuracies with state-of-the-art metric learning methods such as ITML and LMNN but with significantly less training time.

Includes members:
  Member "efficient distance metric learning by adaptive sampling and mini batch stochastic gradient descent sgd":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "fast low rank metric learning for large scale and high dimensional data")
  Member "information theoretic semi supervised metric learning via entropy regularization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "affinity guided geometric semi supervised metric learning")
  Member "learning neighborhoods for metric learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a survey on metric learning for feature vectors and structured data")
  Member "positive semidefinite metric learning using boosting like algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep metric learning with bier boosting independent embeddings robustly")
  Member "scalable large margin mahalanobis distance metric learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "ordinal distance metric learning with mds for image ranking")
  Member "metric and kernel learning using a linear transformation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "multi modal music information retrieval augmenting audio analysis with visual computing for improved music video analysis")
  Member "positive semidefinite metric learning with boosting":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a survey on metric learning for feature vectors and structured data")

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)