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: "generalization bounds for metric and similarity learning"
  Abstract: Recently, metric learning and similarity learning have attracted a large amount of interest. Many models and optimisation algorithms have been proposed. However, there is relatively little work on the generalization analysis of such methods. In this paper, we derive novel generalization bounds of metric and similarity learning. In particular, we first show that the generalization analysis reduces to the estimation of the Rademacher average over "sums-of-i.i.d." sample-blocks related to the specific matrix norm. Then, we derive generalization bounds for metric/similarity learning with different matrix-norm regularisers by estimating their specific Rademacher complexities. Our analysis indicates that sparse metric/similarity learning with $L^1$-norm regularisation could lead to significantly better bounds than those with Frobenius-norm regularisation. Our novel generalization analysis develops and refines the techniques of U-statistics and Rademacher complexity analysis.

Connected through hyperedge HE-1 (induced by source paper "supervised categorical metric learning with schatten p norms"):
  with members:
    - "robustness and generalization for metric learning"
    - "robustness and generalization"

Connected through hyperedge HE-2 (induced by source paper "learning local metrics and influential regions for classification"):
  with members:
    - "sample complexity of learning mahalanobis distance metrics"
    - "sparse compositional metric learning"

Connected through hyperedge HE-3 (induced by source paper "metric learning via maximizing the lipschitz margin ratio"):
  with members:
    - "sample complexity of learning mahalanobis distance metrics"

Connected through hyperedge HE-4 (induced by source paper "on pairwise clustering with side information"):
  with members:
    - "active learning on trees and graphs"
    - "see the tree through the lines the shazoo algorithm"
    - "online similarity prediction of networked data from known and unknown graphs"
    - "a tutorial on spectral clustering"
    - "objective criteria for the evaluation of clustering methods"

Connected through hyperedge HE-5 (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"
    - "guaranteed classification via regularized similarity learning"
    - "similarity learning for provably accurate sparse linear classification"
    - "similarity based learning via data driven embeddings"
    - "robustness and generalization"

Connected through hyperedge HE-6 (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"
    - "guaranteed classification via regularized similarity learning"
    - "similarity learning for provably accurate sparse linear classification"

Connected through hyperedge HE-7 (induced by source paper "unregularized online learning algorithms with general loss functions"):
  with members:
    - "consistency analysis of an empirical minimum error entropy algorithm"
    - "on the generalization ability of online learning algorithms for pairwise loss functions"
    - "learning theory approach to minimum error entropy criterion"

Connected through hyperedge HE-8 (induced by source paper "local rademacher complexity bounds based on covering numbers"):
  with members:
    - "multi class svms from tighter data dependent generalization bounds to novel algorithms"
    - "unregularized online learning algorithms with general loss functions"
    - "optimistic rates for learning with a smooth loss"

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)