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: "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.

Connected through hyperedge HE-1 (induced by source paper "temporal model adaptation for person re identification"):
  with members:
    - "learning to rank in person re identification with metric ensembles"
    - "scalable stochastic alternating direction method of multipliers"
    - "hierarchical subquery evaluation for active learning on a graph"
    - "person re identification with correspondence structure learning"
    - "bit scalable deep hashing with regularized similarity learning for image retrieval and person re identification"
    - "person re identification by local maximal occurrence representation and metric learning"
    - "a survey on metric learning for feature vectors and structured data"

Connected through hyperedge HE-2 (induced by source paper "robustness and generalization for metric learning"):
  with members:
    - "sparse compositional metric learning"
    - "a survey on metric learning for feature vectors and structured data"
    - "similarity learning for provably accurate sparse linear classification"
    - "generalization bounds for metric and similarity learning"
    - "similarity based learning via data driven embeddings"
    - "robustness and generalization"

Connected through hyperedge HE-3 (induced by source paper "sample complexity of learning mahalanobis distance metrics"):
  with members:
    - "robustness and generalization for metric learning"
    - "a survey on metric learning for feature vectors and structured data"
    - "similarity learning for provably accurate sparse linear classification"
    - "generalization bounds for metric and similarity learning"

Connected through hyperedge HE-4 (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"
    - "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"
    - "supervised metric learning with generalization guarantees"

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)