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: "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.

Connected through hyperedge HE-1 (induced by source paper "revisiting training strategies and generalization performance in deep metric learning"):
  with members:
    - "ranked list loss for deep metric learning"
    - "metric learning with horde high order regularizer for deep embeddings"
    - "multi similarity loss with general pair weighting for deep metric learning"
    - "hardness aware deep metric learning"
    - "signal to noise ratio a robust distance metric for deep metric learning"
    - "divide and conquer the embedding space for metric learning"
    - "softtriple loss deep metric learning without triplet sampling"
    - "self supervised learning of pretext invariant representations"

Connected through hyperedge HE-2 (induced by source paper "supervised categorical metric learning with schatten p norms"):
  with members:
    - "generalization bounds for metric and similarity learning"
    - "robustness and generalization"

Connected through hyperedge HE-3 (induced by source paper "stability and optimization error of stochastic gradient descent for pairwise learning"):
  with members:
    - "generalization error bounds for noisy iterative algorithms"
    - "data dependent stability of stochastic gradient descent"
    - "train faster generalize better stability of stochastic gradient descent"
    - "one pass auc optimization"
    - "on the generalization ability of online learning algorithms for pairwise loss functions"
    - "almost everywhere algorithmic stability and generalization error"
    - "random feature maps for dot product kernels"
    - "learning theory approach to minimum error entropy criterion"

Connected through hyperedge HE-4 (induced by source paper "sample complexity of learning mahalanobis distance metrics"):
  with members:
    - "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"
    - "generalization bounds for metric and similarity learning"

Connected through hyperedge HE-5 (induced by source paper "geometry aware deep transform"):
  with members:
    - "on the computational efficiency of training neural networks"
    - "learning deep face representation"
    - "robustness and generalization"

Connected through hyperedge HE-6 (induced by source paper "sparse compositional metric learning"):
  with members:
    - "a survey on metric learning for feature vectors and structured data"
    - "parametric local metric learning for nearest neighbor classification"
    - "positive semidefinite metric learning using boosting like algorithms"
    - "robustness and generalization"

Connected through hyperedge HE-7 (induced by source paper "a survey on metric learning for feature vectors and structured data"):
  with members:
    - "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"
    - "guaranteed classification via regularized similarity learning"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "supervised metric learning with generalization guarantees"):
  with members:
    - "similarity learning for provably accurate sparse linear classification"
    - "information theoretic semi supervised metric learning via entropy regularization"
    - "a conditional random field for discriminatively trained finite state string edit distance"
    - "supervised learning with similarity functions"
    - "non sparse linear representations for visual tracking with online reservoir metric learning"
    - "minimizing the misclassification error rate using a surrogate convex loss"
    - "adaptive regularization for weight matrices"
    - "learning neighborhoods for metric learning"

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)