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: "supervised learning with similarity functions"
  Abstract: We address the problem of general supervised learning when data can only be accessed through an (indefinite) similarity function between data points. Existing work on learning with indefinite kernels has concentrated solely on binary/multi-class classification problems. We propose a model that is generic enough to handle any supervised learning task and also subsumes the model previously proposed for classification. We give a "goodness" criterion for similarity functions w.r.t. a given supervised learning task and then adapt a well-known landmarking technique to provide efficient algorithms for supervised learning using "good" similarity functions. We demonstrate the effectiveness of our model on three important super-vised learning problems: a) real-valued regression, b) ordinal regression and c) ranking where we show that our method guarantees bounded generalization error. Furthermore, for the case of real-valued regression, we give a natural goodness definition that, when used in conjunction with a recent result in sparse vector recovery, guarantees a sparse predictor with bounded generalization error. Finally, we report results of our learning algorithms on regression and ordinal regression tasks using non-PSD similarity functions and demonstrate the effectiveness of our algorithms, especially that of the sparse landmark selection algorithm that achieves significantly higher accuracies than the baseline methods while offering reduced computational costs.

Connected through hyperedge HE-1 (induced by source paper "metric and non metric proximity transformations at linear costs"):
  with members:
    - "efficient algorithms and error analysis for the modified nystrom method"
    - "improving cur matrix decomposition and the nystr o m approximation via adaptive sampling"
    - "revisiting the nystrom method for improved large scale machine learning"
    - "similarity based learning via data driven embeddings"

Connected through hyperedge HE-2 (induced by source paper "guaranteed classification via regularized similarity learning"):
  with members:
    - "generalization guarantees for a binary classification framework for two stage multiple kernel learning"
    - "similarity learning for provably accurate sparse linear classification"
    - "generalization bounds for metric and similarity learning"
    - "a binary classification framework for two stage multiple kernel learning"
    - "similarity based learning via data driven embeddings"
    - "regularization techniques for learning with matrices"

Connected through hyperedge HE-3 (induced by source paper "supervised metric learning with generalization guarantees"):
  with members:
    - "robustness and generalization for metric learning"
    - "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"
    - "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)