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: "online learning via sequential complexities"
  Abstract: We consider the problem of sequential prediction and provide tools to study the minimax value of the associated game. Classical statistical learning theory provides several useful complexity measures to study learning with i.i.d. data. Our proposed sequential complexities can be seen as extensions of these measures to the sequential setting. The developed theory is shown to yield precise learning guarantees for the problem of sequential prediction. In particular, we show necessary and sufficient conditions for online learnability in the setting of supervised learning. Several examples show the utility of our framework: we can establish learnability without having to exhibit an explicit online learning algorithm.

Connected through hyperedge HE-1 (induced by source paper "locally adaptive nonparametric online learning"):
  with members:
    - "introduction to online convex optimization"
    - "gated linear networks"
    - "lipschitz adaptivity with multiple learning rates in online learning"
    - "nonparametric online regression while learning the metric"
    - "metagrad multiple learning rates in online learning"
    - "coin betting and parameter free online learning"
    - "metric entropy in competitive on line prediction"
    - "competing with wild prediction rules"

Connected through hyperedge HE-2 (induced by source paper "risk bounds for reservoir computing"):
  with members:
    - "differentiable reservoir computing"
    - "echo state networks are universal"
    - "reservoir computing universality with stochastic inputs"
    - "theory and algorithms for forecasting time series"
    - "spectrally normalized margin bounds for neural networks"
    - "universal discrete time reservoir computers with stochastic inputs and linear readouts using non homogeneous state affine systems"
    - "difference between memory and prediction in linear recurrent networks"
    - "the asymptotic performance of linear echo state neural networks"

Connected through hyperedge HE-3 (induced by source paper "on generalization bounds of a family of recurrent neural networks"):
  with members:
    - "stabilizing gradients for deep neural networks via efficient svd parameterization"
    - "on tighter generalization bound for deep neural networks cnns resnets and beyond"
    - "all you need is beyond a good init exploring better solution for training extremely deep convolutional neural networks with orthonormality and modulation"
    - "orthogonal weight normalization solution to optimization over multiple dependent stiefel manifolds in deep neural networks"
    - "a pac bayesian approach to spectrally normalized margin bounds for neural networks"
    - "spectrally normalized margin bounds for neural networks"
    - "size independent sample complexity of neural networks"
    - "on orthogonality and learning recurrent networks with long term dependencies"

Connected through hyperedge HE-4 (induced by source paper "small loss bounds for online learning with partial information"):
  with members:
    - "make the minority great again first order regret bound for contextual bandits"
    - "thompson sampling for stochastic bandits with graph feedback"
    - "online learning with feedback graphs without the graphs"
    - "learning in games robustness of fast convergence"
    - "bistro an efficient relaxation based method for contextual bandits"
    - "efficient algorithms for adversarial contextual learning"
    - "learning and efficiency in games with dynamic population"
    - "strongly adaptive online learning"

Connected through hyperedge HE-5 (induced by source paper "the extended littlestone s dimension for learning with mistakes and abstentions"):
  with members:
    - "learning to abstain from binary prediction"
    - "distribution independent reliable learning"
    - "beyond disagreement based agnostic active learning"
    - "a tutorial on conformal prediction"

Connected through hyperedge HE-6 (induced by source paper "the computational power of optimization in online learning"):
  with members:
    - "introduction to online convex optimization"
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "a counter example to karlin s strong conjecture for fictitious play"
    - "near optimal algorithms for online matrix prediction"
    - "efficient optimal learning for contextual bandits"
    - "using more data to speed up training time"
    - "sublinear optimization for machine learning"

Connected through hyperedge HE-7 (induced by source paper "falsifiable implies learnable"):
  with members:
    - "universal learning theory"
    - "a stochastic view of optimal regret through minimax duality"

Connected through hyperedge HE-8 (induced by source paper "no regret learnability for piecewise linear losses"):
  with members:
    - "optimization learning and games with predictable sequences"
    - "a stochastic view of optimal regret through minimax duality"

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)