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: "fast rates in statistical and online learning"
  Abstract: The speed with which a learning algorithm converges as it is presented with more data is a central problem in machine learning --- a fast rate of convergence means less data is needed for the same level of performance. The pursuit of fast rates in online and statistical learning has led to the discovery of many conditions in learning theory under which fast learning is possible. We show that most of these conditions are special cases of a single, unifying condition, that comes in two forms: the central condition for 'proper' learning algorithms that always output a hypothesis in the given model, and stochastic mixability for online algorithms that may make predictions outside of the model. We show that under surprisingly weak assumptions both conditions are, in a certain sense, equivalent. The central condition has a re-interpretation in terms of convexity of a set of pseudoprobabilities, linking it to density estimation under misspecification. For bounded losses, we show how the central condition enables a direct proof of fast rates and we prove its equivalence to the Bernstein condition, itself a generalization of the Tsybakov margin condition, both of which have played a central role in obtaining fast rates in statistical learning. Yet, while the Bernstein condition is two-sided, the central condition is one-sided, making it more suitable to deal with unbounded losses. In its stochastic mixability form, our condition generalizes both a stochastic exp-concavity condition identified by Juditsky, Rigollet and Tsybakov and Vovk's notion of mixability. Our unifying conditions thus provide a substantial step towards a characterization of fast rates in statistical learning, similar to how classical mixability characterizes constant regret in the sequential prediction with expert advice setting.

Connected through hyperedge HE-1 (induced by source paper "strength from weakness fast learning using weak supervision"):
  with members:
    - "weakly supervised disentanglement by pairwise similarities"
    - "deep learning recommendation model for personalization and recommendation systems"
    - "generative discriminative complementary learning"
    - "self supervised visual feature learning with deep neural networks a survey"
    - "self supervised learning of pretext invariant representations"
    - "unsupervised representation learning by predicting image rotations"
    - "exploring the limits of weakly supervised pretraining"
    - "generalized cross entropy loss for training deep neural networks with noisy labels"

Connected through hyperedge HE-2 (induced by source paper "fast rates for online prediction with abstention"):
  with members:
    - "fast classification rates without standard margin assumptions"
    - "online learning with abstention"
    - "combining adversarial guarantees and stochastic fast rates in online learning"
    - "the extended littlestone s dimension for learning with mistakes and abstentions"
    - "the sample complexity of learning linear predictors with the squared loss"
    - "follow the leader if you can hedge if you must"

Connected through hyperedge HE-3 (induced by source paper "fast rate pac bayes generalization bounds via shifted rademacher processes"):
  with members:
    - "data dependent pac bayes priors via differential privacy"
    - "exploring generalization in deep learning"
    - "a tight excess risk bound via a unified pac bayesian rademacher shtarkov mdl complexity"
    - "computing nonvacuous generalization bounds for deep stochastic neural networks with many more parameters than training data"
    - "a pac bayesian approach to spectrally normalized margin bounds for neural networks"
    - "a pac bayesian analysis of randomized learning with application to stochastic gradient descent"
    - "a bayesian perspective on generalization and stochastic gradient descent"
    - "a strongly quasiconvex pac bayesian bound"

Connected through hyperedge HE-4 (induced by source paper "adaptive hedging under delayed feedback"):
  with members:
    - "introduction to online convex optimization"
    - "aggregating strategies for long term forecasting"
    - "second order quantile methods for experts and combinatorial games"
    - "follow the leader if you can hedge if you must"
    - "prediction by random walk perturbation"
    - "online learning under delayed feedback"
    - "mirror descent meets fixed share and feels no regret"
    - "an identity for kernel ridge regression"

Connected through hyperedge HE-5 (induced by source paper "beyond least squares fast rates for regularized empirical risk minimization through self concordance"):
  with members:
    - "statistical optimality of stochastic gradient descent on hard learning problems through multiple passes"
    - "logistic regression the importance of being improper"
    - "large scale kernel learning using block coordinate descent"
    - "fast rates with high probability in exp concave statistical learning"
    - "on the complexity of learning with kernels"
    - "logistic regression tight bounds for stochastic and online optimization"
    - "self concordant analysis for logistic regression"

Connected through hyperedge HE-6 (induced by source paper "pac bayes un expected bernstein inequality"):
  with members:
    - "computing nonvacuous generalization bounds for deep stochastic neural networks with many more parameters than training data"
    - "fast rates with high probability in exp concave statistical learning"
    - "fast rates for general unbounded loss functions from erm to generalized bayes"
    - "combining adversarial guarantees and stochastic fast rates in online learning"
    - "from stochastic mixability to fast rates"
    - "a note on the pac bayesian theorem"

Connected through hyperedge HE-7 (induced by source paper "fast classification rates without standard margin assumptions"):
  with members:
    - "the relationship between agnostic selective classification active learning and the disagreement coefficient"
    - "when are epsilon nets small"
    - "refined error bounds for several learning algorithms"

Connected through hyperedge HE-8 (induced by source paper "logistic regression the importance of being improper"):
  with members:
    - "fast rates with high probability in exp concave statistical learning"
    - "from exp concavity to variance control high probability o 1 n rates and high probability online to batch conversion"
    - "sequential probability assignment with binary alphabets and large classes of experts"
    - "logistic regression tight bounds for stochastic and online optimization"
    - "contextual bandit algorithms with supervised learning guarantees"
    - "self concordant analysis for logistic regression"

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)