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: "the interplay between stability and regret in online learning"
  Abstract: This paper considers the stability of online learning algorithms and its implications for learnability (bounded regret). We introduce a novel quantity called {\em forward regret} that intuitively measures how good an online learning algorithm is if it is allowed a one-step look-ahead into the future. We show that given stability, bounded forward regret is equivalent to bounded regret. We also show that the existence of an algorithm with bounded regret implies the existence of a stable algorithm with bounded regret and bounded forward regret. The equivalence results apply to general, possibly non-convex problems. To the best of our knowledge, our analysis provides the first general connection between stability and regret in the online setting that is not restricted to a particular class of algorithms. Our stability-regret connection provides a simple recipe for analyzing regret incurred by any online learning algorithm. Using our framework, we analyze several existing online learning algorithms as well as the "approximate" versions of algorithms like RDA that solve an optimization problem at each iteration. Our proofs are simpler than existing analysis for the respective algorithms, show a clear trade-off between stability and forward regret, and provide tighter regret bounds in some cases. Furthermore, using our recipe, we analyze "approximate" versions of several algorithms such as follow-the-regularized-leader (FTRL) that requires solving an optimization problem at each step.

Connected through hyperedge HE-1 (induced by source paper "adaptive gradient based meta learning methods"):
  with members:
    - "learning to learn stochastic gradient descent with biased regularization"
    - "online meta learning"
    - "introduction to online convex optimization"
    - "leaf a benchmark for federated settings"
    - "online gradient based mixtures for transfer modulation in meta learning"
    - "federated meta learning for recommendation"
    - "prototypical networks for few shot learning"
    - "meta learning and universality deep representations and gradient descent can approximate any learning algorithm"

Connected through hyperedge HE-2 (induced by source paper "online learning via the differential privacy lens"):
  with members:
    - "introduction to online convex optimization"
    - "private pac learning implies finite littlestone dimension"
    - "make the minority great again first order regret bound for contextual bandits"
    - "achieving privacy in the adversarial multi armed bandit"
    - "the price of differential privacy for online learning"
    - "adaptive learning with robust generalization guarantees"
    - "fighting bandits with a new kind of smoothness"
    - "on the generalization properties of differential privacy"

Connected through hyperedge HE-3 (induced by source paper "online learning via differential privacy"):
  with members:
    - "make the minority great again first order regret bound for contextual bandits"
    - "improved regret bounds for oracle based adversarial contextual bandits"
    - "pca with gaussian perturbations"
    - "fighting bandits with a new kind of smoothness"
    - "on the generalization properties of differential privacy"
    - "train faster generalize better stability of stochastic gradient descent"
    - "algorithmic stability for adaptive data analysis"
    - "first order regret bounds for combinatorial semi bandits"

Connected through hyperedge HE-4 (induced by source paper "reinforcement and imitation learning via interactive no regret learning"):
  with members:
    - "approximate policy iteration schemes a comparison"
    - "learning policies for contextual submodular prediction"
    - "decoupling exploration and exploitation in multi armed bandits"
    - "projection free online learning"
    - "stability conditions for online learnability"
    - "a reduction of imitation learning and structured prediction to no regret online learning"
    - "a contextual bandit approach to personalized news article recommendation"
    - "contextual bandit algorithms with supervised learning guarantees"

Connected through hyperedge HE-5 (induced by source paper "online and stochastic gradient methods for non decomposable loss functions"):
  with members:
    - "on the generalization ability of online learning algorithms for pairwise loss functions"
    - "optimizing f measure a tale of two approaches"
    - "optimal distributed online prediction using mini batches"

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)